import torch import torchvision from torch import nn, optim from transformers import AutoTokenizer, CLIPTextModelWithProjection, CLIPVisionModelWithProjection from warmup_scheduler import GradualWarmupScheduler import sys import os import re from dataclasses import dataclass from gdf import GDF, EpsilonTarget, CosineSchedule from gdf import VPScaler, CosineTNoiseCond, DDPMSampler, P2LossWeight, AdaptiveLossWeight from torchtools.transforms import SmartCrop from modules.effnet import EfficientNetEncoder from modules.stage_c import StageC from modules.stage_c import ResBlock, AttnBlock, TimestepBlock, FeedForwardBlock from modules.previewer import Previewer from modules.lora import apply_lora, apply_retoken, LoRA, ReToken from train.base import DataCore, TrainingCore from core import WarpCore from core.utils import EXPECTED, EXPECTED_TRAIN, load_or_fail from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, ShardingStrategy from torch.distributed.fsdp.wrap import ModuleWrapPolicy from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy import functools from accelerate import init_empty_weights from accelerate.utils import set_module_tensor_to_device from contextlib import contextmanager class WurstCore(TrainingCore, DataCore, WarpCore): @dataclass(frozen=True) class Config(TrainingCore.Config, DataCore.Config, WarpCore.Config): # TRAINING PARAMS lr: float = EXPECTED_TRAIN warmup_updates: int = EXPECTED_TRAIN dtype: str = None # MODEL VERSION model_version: str = EXPECTED # 3.6B or 1B clip_image_model_name: str = 'openai/clip-vit-large-patch14' clip_text_model_name: str = 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' # CHECKPOINT PATHS effnet_checkpoint_path: str = EXPECTED previewer_checkpoint_path: str = EXPECTED generator_checkpoint_path: str = None lora_checkpoint_path: str = None # LoRA STUFF module_filters: list = EXPECTED rank: int = EXPECTED train_tokens: list = EXPECTED # gdf customization adaptive_loss_weight: str = None @dataclass(frozen=True) class Models(TrainingCore.Models, DataCore.Models, WarpCore.Models): effnet: nn.Module = EXPECTED previewer: nn.Module = EXPECTED lora: nn.Module = EXPECTED @dataclass(frozen=True) class Schedulers(WarpCore.Schedulers): lora: any = None @dataclass(frozen=True) class Extras(TrainingCore.Extras, DataCore.Extras, WarpCore.Extras): gdf: GDF = EXPECTED sampling_configs: dict = EXPECTED effnet_preprocess: torchvision.transforms.Compose = EXPECTED @dataclass() # not frozen, means that fields are mutable. Doesn't support EXPECTED class Info(TrainingCore.Info): train_tokens: list = None @dataclass(frozen=True) class Optimizers(TrainingCore.Optimizers, WarpCore.Optimizers): generator: any = None lora: any = EXPECTED # -------------------------------------------- info: Info config: Config # Extras: gdf, transforms and preprocessors -------------------------------- def setup_extras_pre(self) -> Extras: gdf = GDF( schedule=CosineSchedule(clamp_range=[0.0001, 0.9999]), input_scaler=VPScaler(), target=EpsilonTarget(), noise_cond=CosineTNoiseCond(), loss_weight=AdaptiveLossWeight() if self.config.adaptive_loss_weight is True else P2LossWeight(), ) sampling_configs = {"cfg": 5, "sampler": DDPMSampler(gdf), "shift": 1, "timesteps": 20} if self.info.adaptive_loss is not None: gdf.loss_weight.bucket_ranges = torch.tensor(self.info.adaptive_loss['bucket_ranges']) gdf.loss_weight.bucket_losses = torch.tensor(self.info.adaptive_loss['bucket_losses']) effnet_preprocess = torchvision.transforms.Compose([ torchvision.transforms.Normalize( mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) ) ]) clip_preprocess = torchvision.transforms.Compose([ torchvision.transforms.Resize(224, interpolation=torchvision.transforms.InterpolationMode.BICUBIC), torchvision.transforms.CenterCrop(224), torchvision.transforms.Normalize( mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711) ) ]) if self.config.training: transforms = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Resize(self.config.image_size, interpolation=torchvision.transforms.InterpolationMode.BILINEAR, antialias=True), SmartCrop(self.config.image_size, randomize_p=0.3, randomize_q=0.2) ]) else: transforms = None return self.Extras( gdf=gdf, sampling_configs=sampling_configs, transforms=transforms, effnet_preprocess=effnet_preprocess, clip_preprocess=clip_preprocess ) # Data -------------------------------- def get_conditions(self, batch: dict, models: Models, extras: Extras, is_eval=False, is_unconditional=False, eval_image_embeds=False, return_fields=None): conditions = super().get_conditions( batch, models, extras, is_eval, is_unconditional, eval_image_embeds, return_fields=return_fields or ['clip_text', 'clip_text_pooled', 'clip_img'] ) return conditions # Models, Optimizers & Schedulers setup -------------------------------- def setup_models(self, extras: Extras) -> Models: dtype = getattr(torch, self.config.dtype) if self.config.dtype else torch.float32 # EfficientNet encoder effnet = EfficientNetEncoder().to(self.device) effnet_checkpoint = load_or_fail(self.config.effnet_checkpoint_path) effnet.load_state_dict(effnet_checkpoint if 'state_dict' not in effnet_checkpoint else effnet_checkpoint['state_dict']) effnet.eval().requires_grad_(False) del effnet_checkpoint # Previewer previewer = Previewer().to(self.device) previewer_checkpoint = load_or_fail(self.config.previewer_checkpoint_path) previewer.load_state_dict(previewer_checkpoint if 'state_dict' not in previewer_checkpoint else previewer_checkpoint['state_dict']) previewer.eval().requires_grad_(False) del previewer_checkpoint @contextmanager def dummy_context(): yield None loading_context = dummy_context if self.config.training else init_empty_weights with loading_context(): # Diffusion models if self.config.model_version == '3.6B': generator = StageC() elif self.config.model_version == '1B': generator = StageC(c_cond=1536, c_hidden=[1536, 1536], nhead=[24, 24], blocks=[[4, 12], [12, 4]]) else: raise ValueError(f"Unknown model version {self.config.model_version}") if self.config.generator_checkpoint_path is not None: if loading_context is dummy_context: generator.load_state_dict(load_or_fail(self.config.generator_checkpoint_path)) else: for param_name, param in load_or_fail(self.config.generator_checkpoint_path).items(): set_module_tensor_to_device(generator, param_name, "cpu", value=param) generator = generator.to(dtype).to(self.device) generator = self.load_model(generator, 'generator') # if self.config.use_fsdp: # fsdp_auto_wrap_policy = functools.partial(size_based_auto_wrap_policy, min_num_params=3000) # generator = FSDP(generator, **self.fsdp_defaults, auto_wrap_policy=fsdp_auto_wrap_policy, device_id=self.device) # CLIP encoders tokenizer = AutoTokenizer.from_pretrained(self.config.clip_text_model_name) text_model = CLIPTextModelWithProjection.from_pretrained(self.config.clip_text_model_name).requires_grad_(False).to(dtype).to(self.device) image_model = CLIPVisionModelWithProjection.from_pretrained(self.config.clip_image_model_name).requires_grad_(False).to(dtype).to(self.device) # PREPARE LORA update_tokens = [] for tkn_regex, aggr_regex in self.config.train_tokens: if (tkn_regex.startswith('[') and tkn_regex.endswith(']')) or (tkn_regex.startswith('<') and tkn_regex.endswith('>')): # Insert new token tokenizer.add_tokens([tkn_regex]) # add new zeros embedding new_embedding = torch.zeros_like(text_model.text_model.embeddings.token_embedding.weight.data)[:1] if aggr_regex is not None: # aggregate embeddings to provide an interesting baseline aggr_tokens = [v for k, v in tokenizer.vocab.items() if re.search(aggr_regex, k) is not None] if len(aggr_tokens) > 0: new_embedding = text_model.text_model.embeddings.token_embedding.weight.data[aggr_tokens].mean(dim=0, keepdim=True) elif self.is_main_node: print(f"WARNING: No tokens found for aggregation regex {aggr_regex}. It will be initialized as zeros.") text_model.text_model.embeddings.token_embedding.weight.data = torch.cat([ text_model.text_model.embeddings.token_embedding.weight.data, new_embedding ], dim=0) selected_tokens = [len(tokenizer.vocab) - 1] else: selected_tokens = [v for k, v in tokenizer.vocab.items() if re.search(tkn_regex, k) is not None] update_tokens += selected_tokens update_tokens = list(set(update_tokens)) # remove duplicates apply_retoken(text_model.text_model.embeddings.token_embedding, update_tokens) apply_lora(generator, filters=self.config.module_filters, rank=self.config.rank) text_model.text_model.to(self.device) generator.to(self.device) lora = nn.ModuleDict() lora['embeddings'] = text_model.text_model.embeddings.token_embedding.parametrizations.weight[0] lora['weights'] = nn.ModuleList() for module in generator.modules(): if isinstance(module, LoRA) or (hasattr(module, '_fsdp_wrapped_module') and isinstance(module._fsdp_wrapped_module, LoRA)): lora['weights'].append(module) self.info.train_tokens = [(i, tokenizer.decode(i)) for i in update_tokens] if self.is_main_node: print("Updating tokens:", self.info.train_tokens) print(f"LoRA training {len(lora['weights'])} layers") if self.config.lora_checkpoint_path is not None: lora_checkpoint = load_or_fail(self.config.lora_checkpoint_path) lora.load_state_dict(lora_checkpoint if 'state_dict' not in lora_checkpoint else lora_checkpoint['state_dict']) lora = self.load_model(lora, 'lora') lora.to(self.device).train().requires_grad_(True) if self.config.use_fsdp: # fsdp_auto_wrap_policy = functools.partial(size_based_auto_wrap_policy, min_num_params=3000) fsdp_auto_wrap_policy = ModuleWrapPolicy([LoRA, ReToken]) lora = FSDP(lora, **self.fsdp_defaults, auto_wrap_policy=fsdp_auto_wrap_policy, device_id=self.device) return self.Models( effnet=effnet, previewer=previewer, generator=generator, generator_ema=None, lora=lora, tokenizer=tokenizer, text_model=text_model, image_model=image_model ) def setup_optimizers(self, extras: Extras, models: Models) -> Optimizers: optimizer = optim.AdamW(models.lora.parameters(), lr=self.config.lr) # , eps=1e-7, betas=(0.9, 0.95)) optimizer = self.load_optimizer(optimizer, 'lora_optim', fsdp_model=models.lora if self.config.use_fsdp else None) return self.Optimizers(generator=None, lora=optimizer) def setup_schedulers(self, extras: Extras, models: Models, optimizers: Optimizers) -> Schedulers: scheduler = GradualWarmupScheduler(optimizers.lora, multiplier=1, total_epoch=self.config.warmup_updates) scheduler.last_epoch = self.info.total_steps return self.Schedulers(lora=scheduler) def forward_pass(self, data: WarpCore.Data, extras: Extras, models: Models): batch = next(data.iterator) conditions = self.get_conditions(batch, models, extras) with torch.no_grad(): latents = self.encode_latents(batch, models, extras) noised, noise, target, logSNR, noise_cond, loss_weight = extras.gdf.diffuse(latents, shift=1, loss_shift=1) with torch.cuda.amp.autocast(dtype=torch.bfloat16): pred = models.generator(noised, noise_cond, **conditions) loss = nn.functional.mse_loss(pred, target, reduction='none').mean(dim=[1, 2, 3]) loss_adjusted = (loss * loss_weight).mean() / self.config.grad_accum_steps if isinstance(extras.gdf.loss_weight, AdaptiveLossWeight): extras.gdf.loss_weight.update_buckets(logSNR, loss) return loss, loss_adjusted def backward_pass(self, update, loss, loss_adjusted, models: Models, optimizers: TrainingCore.Optimizers, schedulers: Schedulers): if update: loss_adjusted.backward() grad_norm = nn.utils.clip_grad_norm_(models.lora.parameters(), 1.0) optimizers_dict = optimizers.to_dict() for k in optimizers_dict: if optimizers_dict[k] is not None and k != 'training': optimizers_dict[k].step() schedulers_dict = schedulers.to_dict() for k in schedulers_dict: if k != 'training': schedulers_dict[k].step() for k in optimizers_dict: if optimizers_dict[k] is not None and k != 'training': optimizers_dict[k].zero_grad(set_to_none=True) self.info.total_steps += 1 else: loss_adjusted.backward() grad_norm = torch.tensor(0.0).to(self.device) return grad_norm def models_to_save(self): return ['lora'] def sample(self, models: Models, data: WarpCore.Data, extras: Extras): models.lora.eval() super().sample(models, data, extras) models.lora.train(), models.generator.eval() def encode_latents(self, batch: dict, models: Models, extras: Extras) -> torch.Tensor: images = batch['images'].to(self.device) return models.effnet(extras.effnet_preprocess(images)) def decode_latents(self, latents: torch.Tensor, batch: dict, models: Models, extras: Extras) -> torch.Tensor: return models.previewer(latents) if __name__ == '__main__': print("Launching Script") warpcore = WurstCore( config_file_path=sys.argv[1] if len(sys.argv) > 1 else None, device=torch.device(int(os.environ.get("SLURM_LOCALID"))) ) warpcore.fsdp_defaults['sharding_strategy'] = ShardingStrategy.NO_SHARD # RUN TRAINING warpcore()