From 83808fe00ac891ad2f625388d144c318b2cb5bfe Mon Sep 17 00:00:00 2001 From: Volpeon Date: Sat, 14 Jan 2023 21:53:07 +0100 Subject: WIP: Modularization ("free(): invalid pointer" my ass) --- training/common.py | 370 ----------------------------------------------------- 1 file changed, 370 deletions(-) delete mode 100644 training/common.py (limited to 'training/common.py') diff --git a/training/common.py b/training/common.py deleted file mode 100644 index 5d1e3f9..0000000 --- a/training/common.py +++ /dev/null @@ -1,370 +0,0 @@ -import math -from contextlib import _GeneratorContextManager, nullcontext -from typing import Callable, Any, Tuple, Union - -import torch -import torch.nn.functional as F -from torch.utils.data import DataLoader - -from accelerate import Accelerator -from transformers import CLIPTextModel -from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler - -from tqdm.auto import tqdm - -from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion -from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings -from models.clip.util import get_extended_embeddings -from models.clip.tokenizer import MultiCLIPTokenizer -from training.util import AverageMeter, CheckpointerBase - - -def noop(*args, **kwards): - pass - - -def noop_ctx(*args, **kwards): - return nullcontext() - - -def noop_on_log(): - return {} - - -def generate_class_images( - accelerator, - text_encoder, - vae, - unet, - tokenizer, - scheduler, - data_train, - sample_batch_size, - sample_image_size, - sample_steps -): - missing_data = [item for item in data_train if not item.class_image_path.exists()] - - if len(missing_data) == 0: - return - - batched_data = [ - missing_data[i:i+sample_batch_size] - for i in range(0, len(missing_data), sample_batch_size) - ] - - pipeline = VlpnStableDiffusion( - text_encoder=text_encoder, - vae=vae, - unet=unet, - tokenizer=tokenizer, - scheduler=scheduler, - ).to(accelerator.device) - pipeline.set_progress_bar_config(dynamic_ncols=True) - - with torch.inference_mode(): - for batch in batched_data: - image_name = [item.class_image_path for item in batch] - prompt = [item.cprompt for item in batch] - nprompt = [item.nprompt for item in batch] - - images = pipeline( - prompt=prompt, - negative_prompt=nprompt, - height=sample_image_size, - width=sample_image_size, - num_inference_steps=sample_steps - ).images - - for i, image in enumerate(images): - image.save(image_name[i]) - - del pipeline - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - -def get_models(pretrained_model_name_or_path: str): - tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer') - text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder') - vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae') - unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder='unet') - noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder='scheduler') - sample_scheduler = DPMSolverMultistepScheduler.from_pretrained( - pretrained_model_name_or_path, subfolder='scheduler') - - vae.enable_slicing() - vae.set_use_memory_efficient_attention_xformers(True) - unet.set_use_memory_efficient_attention_xformers(True) - - embeddings = patch_managed_embeddings(text_encoder) - - return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings - - -def add_placeholder_tokens( - tokenizer: MultiCLIPTokenizer, - embeddings: ManagedCLIPTextEmbeddings, - placeholder_tokens: list[str], - initializer_tokens: list[str], - num_vectors: Union[list[int], int] -): - initializer_token_ids = [ - tokenizer.encode(token, add_special_tokens=False) - for token in initializer_tokens - ] - placeholder_token_ids = tokenizer.add_multi_tokens(placeholder_tokens, num_vectors) - - embeddings.resize(len(tokenizer)) - - for (placeholder_token_id, initializer_token_id) in zip(placeholder_token_ids, initializer_token_ids): - embeddings.add_embed(placeholder_token_id, initializer_token_id) - - return placeholder_token_ids, initializer_token_ids - - -def loss_step( - vae: AutoencoderKL, - noise_scheduler: DDPMScheduler, - unet: UNet2DConditionModel, - text_encoder: CLIPTextModel, - prior_loss_weight: float, - seed: int, - step: int, - batch: dict[str, Any], - eval: bool = False -): - # Convert images to latent space - latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() - latents = latents * 0.18215 - - generator = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None - - # Sample noise that we'll add to the latents - noise = torch.randn( - latents.shape, - dtype=latents.dtype, - layout=latents.layout, - device=latents.device, - generator=generator - ) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint( - 0, - noise_scheduler.config.num_train_timesteps, - (bsz,), - generator=generator, - device=latents.device, - ) - timesteps = timesteps.long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - noisy_latents = noisy_latents.to(dtype=unet.dtype) - - # Get the text embedding for conditioning - encoder_hidden_states = get_extended_embeddings( - text_encoder, - batch["input_ids"], - batch["attention_mask"] - ) - encoder_hidden_states = encoder_hidden_states.to(dtype=unet.dtype) - - # Predict the noise residual - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - if batch["with_prior"].all(): - # Chunk the noise and model_pred into two parts and compute the loss on each part separately. - model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) - target, target_prior = torch.chunk(target, 2, dim=0) - - # Compute instance loss - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - # Compute prior loss - prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") - - # Add the prior loss to the instance loss. - loss = loss + prior_loss_weight * prior_loss - else: - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - acc = (model_pred == target).float().mean() - - return loss, acc, bsz - - -def train_loop( - accelerator: Accelerator, - optimizer: torch.optim.Optimizer, - lr_scheduler: torch.optim.lr_scheduler._LRScheduler, - model: torch.nn.Module, - checkpointer: CheckpointerBase, - train_dataloader: DataLoader, - val_dataloader: DataLoader, - loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]], - sample_frequency: int = 10, - checkpoint_frequency: int = 50, - global_step_offset: int = 0, - num_epochs: int = 100, - on_log: Callable[[], dict[str, Any]] = noop_on_log, - on_train: Callable[[int], _GeneratorContextManager] = noop_ctx, - on_before_optimize: Callable[[int], None] = noop, - on_after_optimize: Callable[[float], None] = noop, - on_eval: Callable[[], _GeneratorContextManager] = noop_ctx -): - num_training_steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.gradient_accumulation_steps) - num_val_steps_per_epoch = len(val_dataloader) - - num_training_steps = num_training_steps_per_epoch * num_epochs - num_val_steps = num_val_steps_per_epoch * num_epochs - - global_step = 0 - - avg_loss = AverageMeter() - avg_acc = AverageMeter() - - avg_loss_val = AverageMeter() - avg_acc_val = AverageMeter() - - max_acc_val = 0.0 - - local_progress_bar = tqdm( - range(num_training_steps_per_epoch + num_val_steps_per_epoch), - disable=not accelerator.is_local_main_process, - dynamic_ncols=True - ) - local_progress_bar.set_description(f"Epoch 1 / {num_epochs}") - - global_progress_bar = tqdm( - range(num_training_steps + num_val_steps), - disable=not accelerator.is_local_main_process, - dynamic_ncols=True - ) - global_progress_bar.set_description("Total progress") - - try: - for epoch in range(num_epochs): - if accelerator.is_main_process: - if epoch % sample_frequency == 0: - checkpointer.save_samples(global_step + global_step_offset) - - if epoch % checkpoint_frequency == 0 and epoch != 0: - checkpointer.checkpoint(global_step + global_step_offset, "training") - - local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") - local_progress_bar.reset() - - model.train() - - with on_train(epoch): - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(model): - loss, acc, bsz = loss_step(step, batch) - - accelerator.backward(loss) - - on_before_optimize(epoch) - - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad(set_to_none=True) - - avg_loss.update(loss.detach_(), bsz) - avg_acc.update(acc.detach_(), bsz) - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - on_after_optimize(lr_scheduler.get_last_lr()[0]) - - local_progress_bar.update(1) - global_progress_bar.update(1) - - global_step += 1 - - logs = { - "train/loss": avg_loss.avg.item(), - "train/acc": avg_acc.avg.item(), - "train/cur_loss": loss.item(), - "train/cur_acc": acc.item(), - "lr": lr_scheduler.get_last_lr()[0], - } - logs.update(on_log()) - - accelerator.log(logs, step=global_step) - - local_progress_bar.set_postfix(**logs) - - if global_step >= num_training_steps: - break - - accelerator.wait_for_everyone() - - model.eval() - - cur_loss_val = AverageMeter() - cur_acc_val = AverageMeter() - - with torch.inference_mode(), on_eval(): - for step, batch in enumerate(val_dataloader): - loss, acc, bsz = loss_step(step, batch, True) - - loss = loss.detach_() - acc = acc.detach_() - - cur_loss_val.update(loss, bsz) - cur_acc_val.update(acc, bsz) - - avg_loss_val.update(loss, bsz) - avg_acc_val.update(acc, bsz) - - local_progress_bar.update(1) - global_progress_bar.update(1) - - logs = { - "val/loss": avg_loss_val.avg.item(), - "val/acc": avg_acc_val.avg.item(), - "val/cur_loss": loss.item(), - "val/cur_acc": acc.item(), - } - local_progress_bar.set_postfix(**logs) - - logs["val/cur_loss"] = cur_loss_val.avg.item() - logs["val/cur_acc"] = cur_acc_val.avg.item() - - accelerator.log(logs, step=global_step) - - local_progress_bar.clear() - global_progress_bar.clear() - - if accelerator.is_main_process: - if avg_acc_val.avg.item() > max_acc_val: - accelerator.print( - f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}") - checkpointer.checkpoint(global_step + global_step_offset, "milestone") - max_acc_val = avg_acc_val.avg.item() - - # Create the pipeline using using the trained modules and save it. - if accelerator.is_main_process: - print("Finished!") - checkpointer.checkpoint(global_step + global_step_offset, "end") - checkpointer.save_samples(global_step + global_step_offset) - accelerator.end_training() - - except KeyboardInterrupt: - if accelerator.is_main_process: - print("Interrupted") - checkpointer.checkpoint(global_step + global_step_offset, "end") - accelerator.end_training() - quit() -- cgit v1.2.3-54-g00ecf