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hiraishin
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A lightning rod for DL Models
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hiraishin
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# Hiraishin A thin PyTorch-Lightning wrapper for building configuration-based DL pipelines with Hydra. # Dependencies - PyTorch Lightning - Hydra - Pydantic - etc. # Installation ```shell $ pip install -U hiraishin ``` # Basic workflow ## 1. Model initialization with type annotations Define a model class that has training components with type annotations. ```python import torch.nn as nn import torch.optim as optim from hiraishin.models import BaseModel class ToyModel(BaseModel): net: nn.Linear criterion: nn.CrossEntropyLoss optimizer: optim.Adam scheduler: optim.lr_schedulers.ExponentialLR def __init__(self, config: DictConfig) -> None: super().__init__(config) ``` Modules with the following prefixes are instantiated by their own role-specific logic. - `net` - `criterion` - `optimizer` - `scheduler` The same notation can be used to define components other than the learning components listed above (e.g., tokenizers). It is also possible to define built-in type constants that are YAML serializable. ```python class ToyModel(BaseModel): net: nn.Linear criterion: nn.CrossEntropyLoss optimizer: optim.Adam scheduler: optim.lr_schedulers.ExponentialLR # additional components and constants tokenizer: MyTokenizer n_classes: int def __init__(self, config: DictConfig) -> None: super().__init__(config) ``` ## 2. Configuration file generation Hiraishin provides a CLI command that automatically generates a configuration file based on type annotations. For example, if `ToyModel` is defined in `models.py` (i.e., `from models import ToyModel` can be executed in the code), then the following command will generate the configuration file automatically. ```shell $ hiraishin generate model.ToyModel --output_dir config/model The config has been generated! --> config/model/ToyModel.yaml ``` Let's take a look at the generated file. ```yaml _target_: models.ToyModel _recursive_: false config: networks: net: args: _target_: torch.nn.Linear out_features: ??? in_features: ??? weights: initializer: null path: null losses: criterion: args: _target_: torch.nn.CrossEntropyLoss weight: 1.0 optimizers: optimizer: args: _target_: torch.optim.Adam params: - ??? scheduler: args: _target_: torch.optim.lr_scheduler.ExponentialLR gamma: ??? interval: epoch frequency: 1 strict: true monitor: null tokenizer: _target_: MyTokenizer n_classes: ??? ``` First of all, it is compliant with the instantiation by `hydra.utils.instantiate`. The positional arguments are filled with `???` that indicates mandatory parameters. They should be overridden by the values you want to set. ## 3. Training routines definition The rest of model definition is only defining your training routine along with the style of PyTorch Lightning. ```python class ToyModel(BaseModel): ... def forward(self, x: torch.Tensor) -> torch.Tensor: return self.net(x) def training_step(self, batch, *args, **kwargs) -> torch.Tensor: x, target = batch pred = self.forward(x) loss = self.criterion(pred, target) self.log('loss/train', loss) return loss ``` ## 4. Model Instantiation The defined model can be instantiated from configuration file. Let's train your models! ```python from hydra.utils import inatantiate from omegeconf import OmegaConf def app(): ... config = OmegaConf.load('config/model/toy.yaml') model = inatantiate(config) print(model) # ToyModel( # (net): Linear(in_features=1, out_features=1, bias=True) # (criterion): CrossEntropyLoss() # ) trainer.fit(model, ...) ``` ## 5. Model loading You can easily load trained models by using the checkpoints generated by PyTorch Lightning's standard features. Let's test your models! ```python from hiraishin.utils import load_from_checkpoint model = load_from_checkpoint('path/to/model.ckpt') print(model) # ToyModel( # (net): Linear(in_features=1, out_features=1, bias=True) # (criterion): CrossEntropyLoss() # ) ``` # License Hiraishin is licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for the full license text.
