from constants import TRAINER_DIR, TOKENIZER_PATH, DATAMODULE_PATH, WANDB_DIR, RESOURCES from data_generator import generate_data from data_preprocessing import LatexImageDataModule from model import Transformer from utils import LogImageTexCallback, average_checkpoints import argparse import os from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint from pytorch_lightning.loggers import WandbLogger from pytorch_lightning import Trainer import torch # TODO: make label smoothing scale with random magnitude, generate photorealistic images with notebook background def check_setup(): # Disabling tokenizers parallelism because it can't be used before forking and I didn't bother to figure it out os.environ["TOKENIZERS_PARALLELISM"] = "false" if not os.path.isfile(DATAMODULE_PATH): print("Generating default datamodule") datamodule = LatexImageDataModule(image_width=1024, image_height=128, batch_size=16, random_magnitude=5) torch.save(datamodule, DATAMODULE_PATH) if not os.path.isfile(TOKENIZER_PATH): print("Generating default tokenizer") datamodule = torch.load(DATAMODULE_PATH) datamodule.train_tokenizer() def parse_args(): parser = argparse.ArgumentParser(description="Workflow: generate dataset, create datamodule, train model", allow_abbrev=True, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument( "gpus", type=int, help=f"Ids of gpus in range 0..{torch.cuda.device_count() - 1} to train on, " "if not provided,\nthen trains on cpu. To see current gpu load, run nvtop", nargs="*") parser.add_argument( "-l", "-log", help="Whether to save logs of run to w&b logger, default False", default=False, action="store_true", dest="log") parser.add_argument( "-m", "-max-epochs", help="Limit the number of training epochs", type=int, dest="max_epochs") data_args = ["size", "depth", "length", "fraction"] parser.add_argument( "-n", metavar=tuple(map(str.upper, data_args)), nargs=4, dest="data_args", type=lambda x: int(x) if x.isdigit() else float(x), help="Clear old dataset, create new and exit, args:" "\nsize\tsize of new dataset" "\ndepth\tmax_depth scope depth of generated equation, no less than 1" "\nlength\tlength of equation will be in range length/2..length" "\nfraction\tfraction of tex vocab to sample tokens from, float in range 0..1") datamodule = torch.load(DATAMODULE_PATH) datamodule_args = ["image_width", "image_height", "batch_size", "random_magnitude"] parser.add_argument( "-d", metavar=tuple(map(str.upper, datamodule_args)), nargs=4, dest="datamodule_args", type=int, help="Create new datamodule and exit, current parameters:\n" + "\n".join(f"{arg}\t{datamodule.hparams[arg]}" for arg in datamodule_args)) transformer_args = [("num_encoder_layers", 6), ("num_decoder_layers", 6), ("d_model", 512), ("nhead", 8), ("dim_feedforward", 2048), ("dropout", 0.1)] parser.add_argument( "-t", metavar=tuple(args[0].upper() for args in transformer_args), dest="transformer_args", nargs=len(transformer_args), help="Transformer init args, default values:\n" + "\n".join(f"{k}\t{v}" for k, v in transformer_args)) args = parser.parse_args() if args.data_args: args.data_args = dict(zip(data_args, args.data_args)) if args.datamodule_args: args.datamodule_args = dict(zip(datamodule_args, args.datamodule_args)) if args.transformer_args: args.transformer_args = dict(zip(list(zip(*transformer_args))[0], args.transformer_args)) else: args.transformer_args = dict(transformer_args) return args def main(): check_setup() args = parse_args() if args.data_args: generate_data(examples_count=args.data_args['size'], max_depth=args.data_args['depth'], equation_length=args.data_args['length'], distribution_fraction=args.data_args['fraction']) return if args.datamodule_args: datamodule = LatexImageDataModule(image_width=args.datamodule_args["image_width"], image_height=args.datamodule_args["image_height"], batch_size=args.datamodule_args["batch_size"], random_magnitude=args.datamodule_args["random_magnitude"]) datamodule.train_tokenizer() tex_tokenizer = torch.load(TOKENIZER_PATH) print(f"Vocabulary size {tex_tokenizer.get_vocab_size()}") torch.save(datamodule, DATAMODULE_PATH) return datamodule = torch.load(DATAMODULE_PATH) tex_tokenizer = torch.load(TOKENIZER_PATH) logger = None callbacks = [] if args.log: logger = WandbLogger(f"img2tex", save_dir=WANDB_DIR, log_model=True) callbacks = [LogImageTexCallback(logger, top_k=10, max_length=100), LearningRateMonitor(logging_interval="step"), ModelCheckpoint(save_top_k=10, every_n_train_steps=5, monitor="val_loss", mode="min", filename="img2tex-{epoch:02d}-{val_loss:.2f}")] trainer = Trainer(default_root_dir=TRAINER_DIR, max_epochs=args.max_epochs, accelerator="gpu" if args.gpus else "cpu", gpus=args.gpus, logger=logger, strategy="ddp_find_unused_parameters_false", enable_progress_bar=True, callbacks=callbacks) transformer = Transformer(num_encoder_layers=args.transformer_args["num_encoder_layers"], num_decoder_layers=args.transformer_args["num_decoder_layers"], d_model=args.transformer_args["d_model"], nhead=args.transformer_args["nhead"], dim_feedforward=args.transformer_args["dim_feedforward"], dropout=args.transformer_args["dropout"], image_width=datamodule.hparams["image_width"], image_height=datamodule.hparams["image_height"], tgt_vocab_size=tex_tokenizer.get_vocab_size(), pad_idx=tex_tokenizer.token_to_id("[PAD]")) trainer.fit(transformer, datamodule=datamodule) trainer.test(transformer, datamodule=datamodule) if args.log and len(os.listdir(trainer.checkpoint_callback.dirpath)): transformer = average_checkpoints(model_type=Transformer, checkpoints_dir=trainer.checkpoint_callback.dirpath) transformer_path = os.path.join(RESOURCES, f"model_{trainer.logger.version}.pt") transformer.eval() transformer.freeze() torch.save(transformer.state_dict(), transformer_path) print(f"Transformer ensemble saved to '{transformer_path}'") if __name__ == "__main__": main()