# flake8: noqa import hydra import pyrootutils import os import torch from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration from tqdm.auto import tqdm from omegaconf import OmegaConf from omegaconf.dictconfig import DictConfig from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler, \ Transformer2DModel from transformers import CLIPTextModel, CLIPTokenizer import argparse from flask import Flask, request from typing import List, Union import json from typing import Optional import transformers from dataclasses import dataclass, field, asdict, is_dataclass from torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService, DistributedReadingService, \ SequentialReadingService import logging pyrootutils.setup_root(__file__, indicator='.project-root', pythonpath=True) from src.train.schedular import get_scheduler from src.train.dist_utils import all_gather # logger = get_logger(__name__, log_level='info') log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s' logging.basicConfig(level=logging.INFO, format=log_format) logger = logging.getLogger(__name__) # os.environ["WANDB_MODE"] = "offline" @dataclass class ConfigPathArguments: image_transform: Optional[str] = field(default=None, metadata={"help": "config path of image transform"}) sd_image_transform: Optional[str] = field(default=None, metadata={"help": "config path of stable diffusion image transform"}) # tokenizer: Optional[str] = field(default=None, metadata={"help": "config path of tokenizer used to initialize tokenizer"}) visual_encoder: Optional[str] = field(default=None, metadata={"help": "config path of visual encoder"}) # text_encoder: Optional[str] = field(default=None, metadata={"help": "config path of visual encoder"}) discrete_model: Optional[str] = field(default=None, metadata={"help": "config path of discrete model"}) # noise_scheduler: Optional[str] = field(default=None, metadata={"help": "config path of noise scheduler"}) # vae: Optional[str] = field(default=None, metadata={"help": "config path of vae"}) adapter: Optional[str] = field(default=None, metadata={"help": "config path of adapter"}) train_dataset: Optional[str] = field(default=None, metadata={"help": "config path of training dataset"}) fsdp_plugin: Optional[str] = field(default=None, metadata={"help": "config path of fsdp plugin"}) deepspeed_plugin: Optional[str] = field(default=None, metadata={"help": "config path of deepspeed plugin"}) tokenizer: Optional[str] = field(default=None, metadata={"help": "config path of tokenizer used to initialize tokenizer"}) llm_model: Optional[str] = field(default=None, metadata={"help": "config path of llm"}) agent_model: Optional[str] = field(default=None, metadata={"help": "config path of agent"}) @dataclass class TrainingArguments: output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) diffusion_model_path: Optional[str] = field(default=None, metadata={"help": "config path of training dataset"}) resume_from_checkpoint: Optional[str] = field( default=None, metadata={"help": "The path to a folder with a valid checkpoint for your model."}) resume_steps: Optional[int] = field(default=None, metadata={"help": "The training sterps of saved checkpoint"}) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) # adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) # adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) # adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."}) gradient_accumulation_steps: int = field( default=1, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}) mixed_precision: Optional[str] = field( default='no', metadata={ "help": "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU." }) num_train_epochs: int = field(default=3, metadata={"help": "Total number of training epochs to perform."}) max_steps: int = field(default=-1, metadata={"help": "Total number of training steps to perform. "}) save_steps: int = field(default=10000, metadata={"help": "Number of updates steps before two checkpoint saves."}) lr_scheduler_type: str = field(default="cosine", metadata={"help": "The scheduler type to use."}) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) min_lr_ratio: float = field(default=0.01, metadata={"help": "Minimal learning rate ratio."}) dataloader_num_workers: int = field(default=8, metadata={"help": "The number of workers to use for data loading."}) project_name: str = field(default="IPAdapter", metadata={"help": "The name of experiment"}) expr_name: str = field(default="", metadata={"help": "The name of experiment"}) def build_dataloader(dataset_cfg, image_transform, sd_image_transform, tokenizer, dataloader_num_workers=4): dataset = hydra.utils.instantiate(dataset_cfg, image_transform=image_transform, sd_image_transform=sd_image_transform, tokenizer=tokenizer) mp_service = MultiProcessingReadingService(num_workers=dataloader_num_workers) dist_service = DistributedReadingService() reading_service = SequentialReadingService(dist_service, mp_service) dataloader = DataLoader2(dataset, reading_service=reading_service) return dataloader def get_metric(output): metric = {} for key, value in output.items(): if 'loss' in key: metric[key] = value.item() return metric def merge_config(**kwargs): config = {} for key, value in kwargs.items(): if isinstance(value, argparse.Namespace): config[key] = vars(value) elif isinstance(value, DictConfig): config[key] = OmegaConf.to_object(value) elif is_dataclass(value): config[key] = asdict(value) elif isinstance(value, dict): config[key] = value else: logger.error(f'key: {key}, value: {value} will not be merged.') return config def trainable_params(model): count = 0 for name, param in model.named_parameters(): if param.requires_grad: count += param.numel() return count def train(): parser = transformers.HfArgumentParser((ConfigPathArguments, TrainingArguments)) cfg_path, args = parser.parse_args_into_dataclasses() project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=os.path.join(args.output_dir, 'logs')) assert int(cfg_path.fsdp_plugin is not None) + int(cfg_path.deepspeed_plugin is not None) <= 1 if cfg_path.fsdp_plugin is not None: fsdp_plugin_cfg = OmegaConf.load(cfg_path.fsdp_plugin) fsdp_plugin = hydra.utils.instantiate(fsdp_plugin_cfg) logger.info('Use FSDP plugin') else: fsdp_plugin = None if cfg_path.deepspeed_plugin is not None: deepspeed_plugin_cfg = OmegaConf.load(cfg_path.deepspeed_plugin) deepspeed_plugin = hydra.utils.instantiate(deepspeed_plugin_cfg) logger.info('Use deepspeed plugin') else: deepspeed_plugin = None accelerator = Accelerator( mixed_precision=args.mixed_precision, log_with=['tensorboard', 'wandb'], project_config=project_config, gradient_accumulation_steps=args.gradient_accumulation_steps, step_scheduler_with_optimizer=False, fsdp_plugin=fsdp_plugin, deepspeed_plugin=deepspeed_plugin, ) logger.info('Init accelerator done.') if cfg_path.deepspeed_plugin is not None: accelerator.state.deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu'] = 100 os.makedirs(args.output_dir, exist_ok=True) image_transform_cfg = OmegaConf.load(cfg_path.image_transform) image_transform = hydra.utils.instantiate(image_transform_cfg) sd_image_transform_cfg = OmegaConf.load(cfg_path.sd_image_transform) sd_image_transform = hydra.utils.instantiate(sd_image_transform_cfg) tokenizer_cfg = OmegaConf.load(cfg_path.tokenizer) tokenizer = hydra.utils.instantiate(tokenizer_cfg) visual_encoder_cfg = OmegaConf.load(cfg_path.visual_encoder) visual_encoder = hydra.utils.instantiate(visual_encoder_cfg) logger.info('Load visual encoder done.') discrete_model_cfg = OmegaConf.load(cfg_path.discrete_model) discrete_model = hydra.utils.instantiate(discrete_model_cfg) logger.info('Load discrete model done.') # noise_scheduler_cfg = OmegaConf.load(cfg_path.noise_scheduler) # noise_scheduler = hydra.utils.instantiate(noise_scheduler_cfg) # if cfg_path.tokenizer is not None: # tokenizer_cfg = OmegaConf.load(cfg_path.tokenizer) # tokenizer = hydra.utils.instantiate(tokenizer_cfg) # else: # tokenizer_cfg = None # tokenizer = None # if cfg_path.text_encoder is not None: # text_encoder_cfg = OmegaConf.load(cfg_path.text_encoder) # text_encoder = hydra.utils.instantiate(text_encoder_cfg) # logger.info('Load text encoder done.') # else: # text_encoder_cfg = None # text_encoder = None # vae_cfg = OmegaConf.load(cfg_path.vae) # vae = hydra.utils.instantiate(vae_cfg) # logger.info('Load vae done.') # noise_scheduler = DDPMScheduler.from_pretrained(args.diffusion_model_path, subfolder="scheduler") # tokenizer = CLIPTokenizer.from_pretrained(args.diffusion_model_path, subfolder="tokenizer") # text_encoder = CLIPTextModel.from_pretrained(args.diffusion_model_path, subfolder="text_encoder") # vae = AutoencoderKL.from_pretrained(args.diffusion_model_path, subfolder="vae") # unet = UNet2DConditionModel.from_pretrained(args.diffusion_model_path, subfolder="unet") # print('load diffusion model done') # noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(args.diffusion_model_path, subfolder="scheduler") noise_scheduler = DDPMScheduler.from_pretrained(args.diffusion_model_path, subfolder="scheduler") text_encoder = None vae = AutoencoderKL.from_pretrained(args.diffusion_model_path, subfolder="vae") unet = UNet2DConditionModel.from_pretrained(args.diffusion_model_path, subfolder="unet") unet.enable_xformers_memory_efficient_attention() unet.enable_gradient_checkpointing() vae.requires_grad_(False) visual_encoder.requires_grad_(False) discrete_model.requires_grad_(False) adapter_cfg = OmegaConf.load(cfg_path.adapter) adapter = hydra.utils.instantiate(adapter_cfg, unet=unet) logger.info('Load adapter done.') weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 vae.to(accelerator.device, dtype=weight_dtype) visual_encoder.to(accelerator.device, dtype=weight_dtype) discrete_model.to(accelerator.device, dtype=weight_dtype) if text_encoder is not None: text_encoder.to(accelerator.device, dtype=weight_dtype) train_dataset_cfg = OmegaConf.load(cfg_path.train_dataset) train_dataloader = build_dataloader(dataset_cfg=train_dataset_cfg, image_transform=image_transform, sd_image_transform=sd_image_transform, tokenizer=tokenizer, dataloader_num_workers=args.dataloader_num_workers) llm_model_cfg = OmegaConf.load(cfg_path.llm_model) llm_model = hydra.utils.instantiate(llm_model_cfg) llm_model.gradient_checkpointing_enable() llm_model.config.use_cache = False logger.info('Load llm model done.') agent_model_cfg = OmegaConf.load(cfg_path.agent_model) agent_model = hydra.utils.instantiate(agent_model_cfg, llm=llm_model).to(accelerator.device, dtype=weight_dtype) agent_model.requires_grad_(False) agent_model.llm.base_model.model.use_kv_cache_head = False logger.info('Load agent model done.') if cfg_path.fsdp_plugin is not None: adapter = accelerator.prepare(adapter) optimizer = torch.optim.AdamW(adapter.params_to_opt(), lr=args.learning_rate, weight_decay=args.weight_decay) logger.info('Init optimizer done.') scheduler = get_scheduler(name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=args.max_steps, min_lr_ratio=args.min_lr_ratio) # accelerator.register_for_checkpointing(scheduler) # adapter.adapter, adapter.resampler, optimizer, scheduler = accelerator.prepare( # adapter.adapter, # adapter.resampler, # optimizer, # scheduler, # ) # adapter, optimizer, scheduler = accelerator.prepare( # adapter, # optimizer, # scheduler, # ) if cfg_path.fsdp_plugin is not None: optimizer, scheduler = accelerator.prepare(optimizer, scheduler) else: adapter, optimizer, scheduler = accelerator.prepare(adapter, optimizer, scheduler) logger.info('Prepare accelerator done.') # config_record = merge_config(discrete_model=discrete_model_cfg, # visual_encoder=visual_encoder_cfg, # text_encoder=text_encoder_cfg, # image_transform=image_transform_cfg, # sd_image_transform=sd_image_transform_cfg, # tokenizer=tokenizer_cfg, # train_dataset=train_dataset_cfg, # vae=vae_cfg, # adapter=adapter_cfg, # train_args=args) config_record = merge_config(discrete_model=discrete_model_cfg, visual_encoder=visual_encoder_cfg, image_transform=image_transform_cfg, sd_image_transform=sd_image_transform_cfg, train_dataset=train_dataset_cfg, adapter=adapter_cfg, train_args=args, agent_model=agent_model_cfg, llm_model=llm_model, tokenizer=tokenizer_cfg) accelerator.init_trackers(project_name=args.project_name, init_kwargs={"wandb": { "config": config_record, "name": args.expr_name, "dir": args.output_dir }}) if args.resume_from_checkpoint is not None: logger.info(f'Load checkpoint from {args.resume_from_checkpoint}') accelerator.load_state(args.resume_from_checkpoint) num_params = trainable_params(adapter) logger.info("***** Running training *****") logger.info(f" Total optimization steps = {args.max_steps}") logger.info(f" Total trainable params = {num_params}") for name, param in adapter.named_parameters(): if param.requires_grad: print(name) # print(f'adapter: {trainable_params(adapter.adapter)}') # print(f'resampler: {trainable_params(adapter.resampler)}') # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_steps), disable=not accelerator.is_main_process) progress_bar.set_description("Steps") global_step = 0 if args.resume_steps is not None: global_step = args.resume_steps progress_bar.update(args.resume_steps) for epoch in range(args.num_train_epochs): logger.info('Start new epoch') for step, batch in enumerate(train_dataloader): with accelerator.accumulate(adapter): with torch.no_grad(): image_embeds = visual_encoder(batch['images'].to(accelerator.device, dtype=weight_dtype)) image_embeds = discrete_model.encode_image_embeds(image_embeds) if text_encoder is not None: text_embeds = text_encoder(batch['text_input_ids'].to(accelerator.device))[0] else: text_embeds = None latents = vae.encode( batch["sd_images"].to(accelerator.device, dtype=weight_dtype)).latent_dist.sample() latents = latents * vae.config.scaling_factor llm_output = agent_model(input_ids=batch['input_ids'].to(accelerator.device), attention_mask=batch['attention_mask'].to(accelerator.device), labels=batch['labels'].to(accelerator.device), image_embeds=image_embeds, embeds_gen_mask=batch['embeds_gen_mask'].to(accelerator.device) if batch['embeds_gen_mask'] is not None else None, embeds_cmp_mask=batch['embeds_cmp_mask'].to(accelerator.device) if batch['embeds_cmp_mask'] is not None else None, ids_gen_mask=batch['ids_gen_mask'].to(accelerator.device), ids_cmp_mask=batch['ids_cmp_mask'].to(accelerator.device), return_recon_image_embeds=True) time_ids = batch['time_ids'].to(accelerator.device) # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), 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) output = adapter(noisy_latents=noisy_latents, timesteps=timesteps, image_embeds=llm_output['recon_image_embeds'], text_embeds=None, noise=noise, time_ids=time_ids) loss = output['total_loss'] accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(adapter.parameters(), max_norm=args.max_grad_norm) optimizer.step() scheduler.step() optimizer.zero_grad() if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 if global_step % args.save_steps == 0: save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) metric = get_metric(output) metric['lr'] = optimizer.param_groups[0]['lr'] accelerator.log(metric, step=global_step) metric = {key: (format(value, ".6f") if isinstance(value, float) else value) for key, value in metric.items()} # if accelerator.is_local_main_process: if accelerator.is_main_process: tqdm.write(str(metric)) # print(metric) if global_step >= args.max_steps: break accelerator.end_training() if __name__ == '__main__': train()