From 37baa3aa254af721728aa33befdc383858cb8ea2 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Sun, 15 Jan 2023 10:38:49 +0100 Subject: Removed unused code, put training callbacks in dataclass --- train.py | 672 --------------------------------------------------------------- 1 file changed, 672 deletions(-) delete mode 100644 train.py (limited to 'train.py') diff --git a/train.py b/train.py deleted file mode 100644 index d8644c4..0000000 --- a/train.py +++ /dev/null @@ -1,672 +0,0 @@ -import argparse -import datetime -import logging -from pathlib import Path - -import torch -import torch.utils.checkpoint - -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import LoggerType, set_seed -from slugify import slugify - -from data.csv import VlpnDataModule, VlpnDataItem -from util import load_config, load_embeddings_from_dir - -from trainer.ti import TextualInversionTrainingStrategy -from trainer.base import Trainer -from training.optimization import get_scheduler -from training.util import save_args, generate_class_images, add_placeholder_tokens, get_models - -logger = get_logger(__name__) - - -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cudnn.benchmark = True - - -def parse_args(): - parser = argparse.ArgumentParser( - description="Simple example of a training script." - ) - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--train_data_file", - type=str, - default=None, - help="A CSV file containing the training data." - ) - parser.add_argument( - "--train_data_template", - type=str, - default="template", - ) - parser.add_argument( - "--project", - type=str, - default=None, - help="The name of the current project.", - ) - parser.add_argument( - "--placeholder_tokens", - type=str, - nargs='*', - help="A token to use as a placeholder for the concept.", - ) - parser.add_argument( - "--initializer_tokens", - type=str, - nargs='*', - help="A token to use as initializer word." - ) - parser.add_argument( - "--num_vectors", - type=int, - nargs='*', - help="Number of vectors per embedding." - ) - parser.add_argument( - "--num_class_images", - type=int, - default=1, - help="How many class images to generate." - ) - parser.add_argument( - "--class_image_dir", - type=str, - default="cls", - help="The directory where class images will be saved.", - ) - parser.add_argument( - "--exclude_collections", - type=str, - nargs='*', - help="Exclude all items with a listed collection.", - ) - parser.add_argument( - "--output_dir", - type=str, - default="output/text-inversion", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--embeddings_dir", - type=str, - default=None, - help="The embeddings directory where Textual Inversion embeddings are stored.", - ) - parser.add_argument( - "--collection", - type=str, - nargs='*', - help="A collection to filter the dataset.", - ) - parser.add_argument( - "--seed", - type=int, - default=None, - help="A seed for reproducible training." - ) - parser.add_argument( - "--resolution", - type=int, - default=768, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--num_buckets", - type=int, - default=0, - help="Number of aspect ratio buckets in either direction.", - ) - parser.add_argument( - "--progressive_buckets", - action="store_true", - help="Include images in smaller buckets as well.", - ) - parser.add_argument( - "--bucket_step_size", - type=int, - default=64, - help="Step size between buckets.", - ) - parser.add_argument( - "--bucket_max_pixels", - type=int, - default=None, - help="Maximum pixels per bucket.", - ) - parser.add_argument( - "--tag_dropout", - type=float, - default=0, - help="Tag dropout probability.", - ) - parser.add_argument( - "--no_tag_shuffle", - action="store_true", - help="Shuffle tags.", - ) - parser.add_argument( - "--vector_dropout", - type=int, - default=0, - help="Vector dropout probability.", - ) - parser.add_argument( - "--vector_shuffle", - type=str, - default="auto", - help='Vector shuffling algorithm. Choose between ["all", "trailing", "leading", "between", "auto", "off"]', - ) - parser.add_argument( - "--num_train_epochs", - type=int, - default=100 - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--find_lr", - action="store_true", - help="Automatically find a learning rate (no training).", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-4, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="one_cycle", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup", "one_cycle"]' - ), - ) - parser.add_argument( - "--lr_warmup_epochs", - type=int, - default=10, - help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--lr_cycles", - type=int, - default=None, - help="Number of restart cycles in the lr scheduler." - ) - parser.add_argument( - "--lr_warmup_func", - type=str, - default="cos", - help='Choose between ["linear", "cos"]' - ) - parser.add_argument( - "--lr_warmup_exp", - type=int, - default=1, - help='If lr_warmup_func is "cos", exponent to modify the function' - ) - parser.add_argument( - "--lr_annealing_func", - type=str, - default="cos", - help='Choose between ["linear", "half_cos", "cos"]' - ) - parser.add_argument( - "--lr_annealing_exp", - type=int, - default=1, - help='If lr_annealing_func is "half_cos" or "cos", exponent to modify the function' - ) - parser.add_argument( - "--lr_min_lr", - type=float, - default=0.04, - help="Minimum learning rate in the lr scheduler." - ) - parser.add_argument( - "--use_ema", - action="store_true", - help="Whether to use EMA model." - ) - parser.add_argument( - "--ema_inv_gamma", - type=float, - default=1.0 - ) - parser.add_argument( - "--ema_power", - type=float, - default=1 - ) - parser.add_argument( - "--ema_max_decay", - type=float, - default=0.9999 - ) - parser.add_argument( - "--use_8bit_adam", - action="store_true", - help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument( - "--adam_beta1", - type=float, - default=0.9, - help="The beta1 parameter for the Adam optimizer." - ) - parser.add_argument( - "--adam_beta2", - type=float, - default=0.999, - help="The beta2 parameter for the Adam optimizer." - ) - parser.add_argument( - "--adam_weight_decay", - type=float, - default=0, - help="Weight decay to use." - ) - parser.add_argument( - "--adam_epsilon", - type=float, - default=1e-08, - help="Epsilon value for the Adam optimizer" - ) - parser.add_argument( - "--adam_amsgrad", - type=bool, - default=False, - help="Amsgrad value for the Adam optimizer" - ) - parser.add_argument( - "--mixed_precision", - type=str, - default="no", - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose" - "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." - "and an Nvidia Ampere GPU." - ), - ) - parser.add_argument( - "--checkpoint_frequency", - type=int, - default=5, - help="How often to save a checkpoint and sample image (in epochs)", - ) - parser.add_argument( - "--sample_frequency", - type=int, - default=1, - help="How often to save a checkpoint and sample image (in epochs)", - ) - parser.add_argument( - "--sample_image_size", - type=int, - default=768, - help="Size of sample images", - ) - parser.add_argument( - "--sample_batches", - type=int, - default=1, - help="Number of sample batches to generate per checkpoint", - ) - parser.add_argument( - "--sample_batch_size", - type=int, - default=1, - help="Number of samples to generate per batch", - ) - parser.add_argument( - "--valid_set_size", - type=int, - default=None, - help="Number of images in the validation dataset." - ) - parser.add_argument( - "--valid_set_repeat", - type=int, - default=1, - help="Times the images in the validation dataset are repeated." - ) - parser.add_argument( - "--train_batch_size", - type=int, - default=1, - help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_steps", - type=int, - default=20, - help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", - ) - parser.add_argument( - "--prior_loss_weight", - type=float, - default=1.0, - help="The weight of prior preservation loss." - ) - parser.add_argument( - "--emb_decay_target", - default=0.4, - type=float, - help="Embedding decay target." - ) - parser.add_argument( - "--emb_decay_factor", - default=0, - type=float, - help="Embedding decay factor." - ) - parser.add_argument( - "--emb_decay_start", - default=1e-4, - type=float, - help="Embedding decay start offset." - ) - parser.add_argument( - "--noise_timesteps", - type=int, - default=1000, - ) - parser.add_argument( - "--resume_from", - type=str, - default=None, - help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)" - ) - parser.add_argument( - "--global_step", - type=int, - default=0, - ) - parser.add_argument( - "--config", - type=str, - default=None, - help="Path to a JSON configuration file containing arguments for invoking this script." - ) - - args = parser.parse_args() - if args.config is not None: - args = load_config(args.config) - args = parser.parse_args(namespace=argparse.Namespace(**args)) - - if args.train_data_file is None: - raise ValueError("You must specify --train_data_file") - - if args.pretrained_model_name_or_path is None: - raise ValueError("You must specify --pretrained_model_name_or_path") - - if args.project is None: - raise ValueError("You must specify --project") - - if isinstance(args.placeholder_tokens, str): - args.placeholder_tokens = [args.placeholder_tokens] - - if len(args.placeholder_tokens) == 0: - args.placeholder_tokens = [f"<*{i}>" for i in range(args.initializer_tokens)] - - if isinstance(args.initializer_tokens, str): - args.initializer_tokens = [args.initializer_tokens] * len(args.placeholder_tokens) - - if len(args.initializer_tokens) == 0: - raise ValueError("You must specify --initializer_tokens") - - if len(args.placeholder_tokens) != len(args.initializer_tokens): - raise ValueError("--placeholder_tokens and --initializer_tokens must have the same number of items") - - if args.num_vectors is None: - args.num_vectors = 1 - - if isinstance(args.num_vectors, int): - args.num_vectors = [args.num_vectors] * len(args.initializer_tokens) - - if len(args.placeholder_tokens) != len(args.num_vectors): - raise ValueError("--placeholder_tokens and --num_vectors must have the same number of items") - - if isinstance(args.collection, str): - args.collection = [args.collection] - - if isinstance(args.exclude_collections, str): - args.exclude_collections = [args.exclude_collections] - - if args.output_dir is None: - raise ValueError("You must specify --output_dir") - - return args - - -def main(): - args = parse_args() - - global_step_offset = args.global_step - now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - output_dir = Path(args.output_dir).joinpath(slugify(args.project), now) - output_dir.mkdir(parents=True, exist_ok=True) - - accelerator = Accelerator( - log_with=LoggerType.TENSORBOARD, - logging_dir=f"{output_dir}", - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision - ) - - logging.basicConfig(filename=output_dir.joinpath("log.txt"), level=logging.DEBUG) - - if args.seed is None: - args.seed = torch.random.seed() >> 32 - - set_seed(args.seed) - - save_args(output_dir, args) - - tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings = get_models( - args.pretrained_model_name_or_path) - - tokenizer.set_use_vector_shuffle(args.vector_shuffle) - tokenizer.set_dropout(args.vector_dropout) - - vae.enable_slicing() - vae.set_use_memory_efficient_attention_xformers(True) - unet.set_use_memory_efficient_attention_xformers(True) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - text_encoder.gradient_checkpointing_enable() - - if args.embeddings_dir is not None: - embeddings_dir = Path(args.embeddings_dir) - if not embeddings_dir.exists() or not embeddings_dir.is_dir(): - raise ValueError("--embeddings_dir must point to an existing directory") - - added_tokens, added_ids = load_embeddings_from_dir(tokenizer, embeddings, embeddings_dir) - print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}") - - placeholder_token_ids, initializer_token_ids = add_placeholder_tokens( - tokenizer=tokenizer, - embeddings=embeddings, - placeholder_tokens=args.placeholder_tokens, - initializer_tokens=args.initializer_tokens, - num_vectors=args.num_vectors - ) - - if len(placeholder_token_ids) != 0: - initializer_token_id_lens = [len(id) for id in initializer_token_ids] - placeholder_token_stats = list(zip(args.placeholder_tokens, placeholder_token_ids, initializer_token_id_lens)) - print(f"Added {len(placeholder_token_ids)} new tokens: {placeholder_token_stats}") - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * - args.train_batch_size * accelerator.num_processes - ) - - if args.find_lr: - args.learning_rate = 1e-5 - - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") - - optimizer_class = bnb.optim.AdamW8bit - else: - optimizer_class = torch.optim.AdamW - - optimizer = optimizer_class( - text_encoder.text_model.embeddings.temp_token_embedding.parameters(), - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - amsgrad=args.adam_amsgrad, - ) - - weight_dtype = torch.float32 - if args.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif args.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - def keyword_filter(item: VlpnDataItem): - cond1 = any( - keyword in part - for keyword in args.placeholder_tokens - for part in item.prompt - ) - cond3 = args.collection is None or args.collection in item.collection - cond4 = args.exclude_collections is None or not any( - collection in item.collection - for collection in args.exclude_collections - ) - return cond1 and cond3 and cond4 - - datamodule = VlpnDataModule( - data_file=args.train_data_file, - batch_size=args.train_batch_size, - tokenizer=tokenizer, - class_subdir=args.class_image_dir, - num_class_images=args.num_class_images, - size=args.resolution, - num_buckets=args.num_buckets, - progressive_buckets=args.progressive_buckets, - bucket_step_size=args.bucket_step_size, - bucket_max_pixels=args.bucket_max_pixels, - dropout=args.tag_dropout, - shuffle=not args.no_tag_shuffle, - template_key=args.train_data_template, - valid_set_size=args.valid_set_size, - valid_set_repeat=args.valid_set_repeat, - seed=args.seed, - filter=keyword_filter, - dtype=weight_dtype - ) - datamodule.setup() - - train_dataloader = datamodule.train_dataloader - val_dataloader = datamodule.val_dataloader - - if args.num_class_images != 0: - generate_class_images( - accelerator, - text_encoder, - vae, - unet, - tokenizer, - sample_scheduler, - datamodule.data_train, - args.sample_batch_size, - args.sample_image_size, - args.sample_steps - ) - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_training_steps_per_epoch=len(train_dataloader), - gradient_accumulation_steps=args.gradient_accumulation_steps, - min_lr=args.lr_min_lr, - warmup_func=args.lr_warmup_func, - annealing_func=args.lr_annealing_func, - warmup_exp=args.lr_warmup_exp, - annealing_exp=args.lr_annealing_exp, - cycles=args.lr_cycles, - train_epochs=args.num_train_epochs, - warmup_epochs=args.lr_warmup_epochs, - ) - - trainer = Trainer( - accelerator=accelerator, - unet=unet, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - noise_scheduler=noise_scheduler, - sample_scheduler=sample_scheduler, - train_dataloader=train_dataloader, - val_dataloader=val_dataloader, - dtype=weight_dtype, - ) - - trainer( - strategy_class=TextualInversionTrainingStrategy, - optimizer=optimizer, - lr_scheduler=lr_scheduler, - num_train_epochs=args.num_train_epochs, - sample_frequency=args.sample_frequency, - checkpoint_frequency=args.checkpoint_frequency, - global_step_offset=global_step_offset, - prior_loss_weight=args.prior_loss_weight, - output_dir=output_dir, - placeholder_tokens=args.placeholder_tokens, - placeholder_token_ids=placeholder_token_ids, - learning_rate=args.learning_rate, - sample_steps=args.sample_steps, - sample_image_size=args.sample_image_size, - sample_batch_size=args.sample_batch_size, - sample_batches=args.sample_batches, - seed=args.seed, - ) - - -if __name__ == "__main__": - main() -- cgit v1.2.3-54-g00ecf