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"""Moses VAE training pipeline."""
import argparse
import logging
import os
from dataclasses import dataclass, field
from typing import Any, Dict
from guacamol_baselines.moses_baselines.vae_train import main
from ...core import TrainingPipelineArguments
from ..core import MosesTrainingArguments, MosesTrainingPipeline
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
[docs]class MosesVAETrainingPipeline(MosesTrainingPipeline):
"""Moses VAE training pipelines."""
[docs] def train( # type: ignore
self,
training_args: Dict[str, Any],
model_args: Dict[str, Any],
dataset_args: Dict[str, Any],
) -> None:
"""Generic training function for Moses VAE training.
Args:
training_args: training arguments passed to the configuration.
model_args: model arguments passed to the configuration.
dataset_args: dataset arguments passed to the configuration.
"""
params = {**training_args, **model_args, **dataset_args}
os.makedirs(os.path.dirname(params["model_save"]), exist_ok=True)
os.makedirs(os.path.dirname(params["log_file"]), exist_ok=True)
os.makedirs(os.path.dirname(params["config_save"]), exist_ok=True)
os.makedirs(os.path.dirname(params["vocab_save"]), exist_ok=True)
args = argparse.Namespace(**params)
main(args)
[docs]@dataclass
class MosesVAEModelArguments(TrainingPipelineArguments):
"""Arguments related to Moses VAE model."""
__name__ = "model_args"
q_cell: str = field(default="gru", metadata={"help": "Encoder rnn cell type."})
q_bidir: bool = field(
default=True,
metadata={"help": "Whether to add second direction in the encoder."},
)
q_d_h: int = field(default=256, metadata={"help": "Encoder h dimensionality."})
q_n_layers: int = field(default=1, metadata={"help": "Encoder number of layers."})
q_dropout: float = field(default=0.5, metadata={"help": "Encoder layers dropout."})
d_cell: str = field(default="gru", metadata={"help": "Decoder rnn cell type."})
d_n_layers: int = field(default=3, metadata={"help": "Decoder number of layers."})
d_dropout: float = field(default=0, metadata={"help": "Decoder layers dropout"})
d_z: int = field(default=128, metadata={"help": "Latent vector dimensionality"})
d_d_h: int = field(default=512, metadata={"help": "Latent vector dimensionality"})
freeze_embeddings: bool = field(
default=False, metadata={"help": "If to freeze embeddings while training"}
)
[docs]@dataclass
class MosesVAETrainingArguments(MosesTrainingArguments):
"""Arguments related to Moses VAE training."""
n_batch: int = field(default=512, metadata={"help": "Batch size."})
grad_clipping: int = field(
default=50, metadata={"help": "Gradients clipping size."}
)
kl_start: int = field(
default=0, metadata={"help": "Epoch to start change kl weight from."}
)
kl_w_start: float = field(default=0, metadata={"help": "Initial kl weight value."})
kl_w_end: float = field(default=0.05, metadata={"help": "Maximum kl weight value."})
lr_start: float = field(default=3 * 1e-4, metadata={"help": "Initial lr value."})
lr_n_period: int = field(
default=10, metadata={"help": "Epochs before first restart in SGDR."}
)
lr_n_restarts: int = field(
default=10, metadata={"help": "Number of restarts in SGDR."}
)
lr_n_mult: int = field(
default=1, metadata={"help": "Mult coefficient after restart in SGDR."}
)
lr_end: float = field(
default=3 * 1e-4, metadata={"help": "Maximum lr weight value."}
)
n_last: int = field(
default=1000, metadata={"help": "Number of iters to smooth loss calc."}
)
n_jobs: int = field(default=1, metadata={"help": "Number of threads."})
n_workers: int = field(default=1, metadata={"help": "Number of workers."})
warm_start: str = field(
default="",
metadata={
"help": "Path to a folder to warm start from. Set empty string to not use."
"This has to contain files `model.pt`, `vocab.pt` and `config.pt`."
},
)