diff --git a/configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml b/configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml index bb0c934..15f7739 100644 --- a/configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml +++ b/configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml @@ -2,7 +2,6 @@ model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: - ckpt_path: "/home/mchorse/stable-diffusion-ckpts/256pretrain-2022-06-09.ckpt" linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 @@ -20,7 +19,7 @@ model: scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: - warm_up_steps: [ 10000 ] + warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] diff --git a/configs/stable-diffusion/v1_improvedaesthetics.yaml b/configs/stable-diffusion/v1_improvedaesthetics.yaml new file mode 100644 index 0000000..58b9df4 --- /dev/null +++ b/configs/stable-diffusion/v1_improvedaesthetics.yaml @@ -0,0 +1,135 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: "__improvedaesthetic__" + batch_size: 4 + num_workers: 4 + multinode: True + train: + shards: '{00000..17279}.tar -' + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: '{17280..17535}.tar -' + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + + +lightning: + find_unused_parameters: False + + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + image_logger: + target: main.ImageLogger + params: + batch_frequency: 5000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 3.0 + unconditional_guidance_label: [""] + + trainer: + benchmark: True + val_check_interval: 5000000 # really sorry + num_sanity_val_steps: 0 + accumulate_grad_batches: 2 diff --git a/configs/stable-diffusion/v2_laionhr1024.yaml b/configs/stable-diffusion/v2_laionhr1024.yaml new file mode 100644 index 0000000..22837a1 --- /dev/null +++ b/configs/stable-diffusion/v2_laionhr1024.yaml @@ -0,0 +1,132 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.001 + linear_end: 0.015 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 16 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.22765929 # magic number + + # NOTE disabled for resuming + #scheduler_config: # 10000 warmup steps + # target: ldm.lr_scheduler.LambdaLinearScheduler + # params: + # warm_up_steps: [ 10000 ] + # cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + # f_start: [ 1.e-6 ] + # f_max: [ 1. ] + # f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 64 # not really needed + in_channels: 16 + out_channels: 16 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 16 + monitor: val/rec_loss + ddconfig: + double_z: True + z_channels: 16 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [ 16 ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/" + batch_size: 3 + num_workers: 4 + multinode: True + train: + shards: '{00000..17279}.tar -' + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 1024 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 1024 + + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: '{17280..17535}.tar -' + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 1024 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 1024 + + +lightning: + find_unused_parameters: False + + modelcheckpoint: + params: + every_n_train_steps: 2000 + + callbacks: + image_logger: + target: main.ImageLogger + params: + batch_frequency: 2000 + max_images: 2 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 2 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 4 diff --git a/configs/stable-diffusion/v3_pretraining.yaml b/configs/stable-diffusion/v3_pretraining.yaml new file mode 100644 index 0000000..2aa78d4 --- /dev/null +++ b/configs/stable-diffusion/v3_pretraining.yaml @@ -0,0 +1,137 @@ +model: + base_learning_rate: 8.e-05 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 32 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 416 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: [ 2, 2, 2, 2 ] + channel_mult: [ 1, 2, 4, 4 ] + disable_self_attentions: [ False, False, False, False ] # converts the self-attention to a cross-attention layer if true + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ckpt_path: "/fsx/stable-diffusion/stable-diffusion/models/first_stage_models/kl-f8/model.ckpt" + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: "__improvedaesthetic__" + batch_size: 8 + num_workers: 4 + multinode: True + train: + shards: '{00000..17279}.tar -' + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 256 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 256 + +# # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: '{17280..17535}.tar -' + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 256 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 256 + + +lightning: + find_unused_parameters: false + modelcheckpoint: + params: + every_n_train_steps: 5000 + callbacks: + image_logger: + target: main.ImageLogger + params: + disabled: True + batch_frequency: 2500 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 3.0 + unconditional_guidance_label: [""] + + trainer: + #replace_sampler_ddp: False + benchmark: True + val_check_interval: 5000000 # really sorry + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/ldm/data/laion.py b/ldm/data/laion.py index 73d928b..41d6fa3 100644 --- a/ldm/data/laion.py +++ b/ldm/data/laion.py @@ -151,12 +151,7 @@ class WebDataModuleFromConfig(pl.LightningDataModule): if self.tar_base == "__improvedaesthetic__": print("## Warning, loading the same improved aesthetic dataset " "for all splits and ignoring shards parameter.") - urls = [] - for i in range(1, 65): - for j in range(512): - for k in range(5): - urls.append(f's3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics/{i:02d}/{j:03d}/{k:05d}.tar') - tars = [f'pipe:aws s3 cp {url} -' for url in urls] + tars = "pipe:aws s3 cp s3://s-laion/improved-aesthetics-laion-2B-en-subsets/aesthetics_tars/{000000..060207}.tar -" else: tars = os.path.join(self.tar_base, dataset_config.shards) @@ -314,7 +309,8 @@ if __name__ == "__main__": from pytorch_lightning.trainer.supporters import CombinedLoader, CycleIterator #config = OmegaConf.load("configs/stable-diffusion/txt2img-1p4B-multinode-clip-encoder-high-res-512.yaml") - config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml") + #config = OmegaConf.load("configs/stable-diffusion/txt2img-upscale-clip-encoder-f16-1024.yaml") + config = OmegaConf.load("configs/stable-diffusion/txt2img-v2-clip-encoder-improved_aesthetics-256.yaml") datamod = WebDataModuleFromConfig(**config["data"]["params"]) dataloader = datamod.train_dataloader() diff --git a/main.py b/main.py index e8946a5..b190065 100644 --- a/main.py +++ b/main.py @@ -21,6 +21,9 @@ from ldm.data.base import Txt2ImgIterableBaseDataset from ldm.util import instantiate_from_config +MULTINODE_HACKS = True + + def get_parser(**parser_kwargs): def str2bool(v): if isinstance(v, bool): @@ -268,6 +271,9 @@ class SetupCallback(Callback): os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True) print("Project config") print(OmegaConf.to_yaml(self.config)) + if MULTINODE_HACKS: + import time + time.sleep(5) OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now))) @@ -278,7 +284,7 @@ class SetupCallback(Callback): else: # ModelCheckpoint callback created log directory --- remove it - if not self.resume and os.path.exists(self.logdir): + if not MULTINODE_HACKS and not self.resume and os.path.exists(self.logdir): dst, name = os.path.split(self.logdir) dst = os.path.join(dst, "child_runs", name) os.makedirs(os.path.split(dst)[0], exist_ok=True) @@ -759,9 +765,19 @@ if __name__ == "__main__": del callbacks_cfg['ignore_keys_callback'] trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg] + if not "plugins" in trainer_kwargs: + trainer_kwargs["plugins"] = list() if not lightning_config.get("find_unused_parameters", True): from pytorch_lightning.plugins import DDPPlugin - trainer_kwargs["plugins"] = DDPPlugin(find_unused_parameters=False) + trainer_kwargs["plugins"].append(DDPPlugin(find_unused_parameters=False)) + if MULTINODE_HACKS: + # disable resume from hpc ckpts + # NOTE below only works in later versions + # from pytorch_lightning.plugins.environments import SLURMEnvironment + # trainer_kwargs["plugins"].append(SLURMEnvironment(auto_requeue=False)) + # hence we monkey patch things + from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector + setattr(CheckpointConnector, "hpc_resume_path", None) trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs) trainer.logdir = logdir ### @@ -833,6 +849,18 @@ if __name__ == "__main__": raise if not opt.no_test and not trainer.interrupted: trainer.test(model, data) + except RuntimeError as err: + if MULTINODE_HACKS: + import requests + import datetime + import os + import socket + device = os.environ.get("CUDA_VISIBLE_DEVICES", "?") + hostname = socket.gethostname() + ts = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S') + resp = requests.get('http://169.254.169.254/latest/meta-data/instance-id') + print(f'ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}', flush=True) + raise err except Exception: if opt.debug and trainer.global_rank == 0: try: diff --git a/scripts/checker.py b/scripts/checker.py new file mode 100644 index 0000000..53e360e --- /dev/null +++ b/scripts/checker.py @@ -0,0 +1,218 @@ +import os +import glob +import subprocess +import time +import fire + +import numpy as np +from tqdm import tqdm +import torch +from pytorch_lightning import seed_everything +from omegaconf import OmegaConf +from ldm.util import instantiate_from_config +from ldm.models.diffusion.plms import PLMSSampler +from einops import rearrange +from torchvision.utils import make_grid +from PIL import Image +import contextlib + + +def load_model_from_config(config, ckpt, verbose=False): + pl_sd = torch.load(ckpt, map_location="cpu") + gs = pl_sd["global_step"] + sd = pl_sd["state_dict"] + model = instantiate_from_config(config.model) + m, u = model.load_state_dict(sd, strict=True) + model.cuda() + model.eval() + return model, gs + + +def read_prompts(path): + with open(path, "r") as f: + prompts = f.read().splitlines() + return prompts + + +def split_in_batches(iterator, n): + out = [] + for elem in iterator: + out.append(elem) + if len(out) == n: + yield out + out = [] + if len(out) > 0: + yield out + + +class Sampler(object): + def __init__(self, out_dir, ckpt_path, cfg_path, prompts_path, shape, seed=42): + self.out_dir = out_dir + self.ckpt_path = ckpt_path + self.cfg_path = cfg_path + self.prompts_path = prompts_path + self.seed = seed + + self.batch_size = 1 + self.scale = 10 + self.shape = shape + self.n_steps = 100 + self.nrow = 8 + + + @torch.inference_mode() + def sample(self, model, prompts, ema=True): + seed = self.seed + batch_size = self.batch_size + scale = self.scale + n_steps = self.n_steps + + shape = self.shape + + print("Sampling model.") + print("ckpt_path", self.ckpt_path) + print("cfg_path", self.cfg_path) + print("prompts_path", self.prompts_path) + print("out_dir", self.out_dir) + print("seed", self.seed) + print("batch_size", batch_size) + print("scale", scale) + print("n_steps", n_steps) + print("shape", shape) + + prompts = list(split_in_batches(prompts, batch_size)) + + sampler = PLMSSampler(model) + all_samples = list() + + ctxt = model.ema_scope if ema else contextlib.nullcontext + + with ctxt(): + for prompts_batch in tqdm(prompts, desc="prompts"): + uc = None + if scale != 1.0: + uc = model.get_learned_conditioning(batch_size * [""]) + c = model.get_learned_conditioning(prompts_batch) + + seed_everything(seed) + + samples_latent, _ = sampler.sample( + S=n_steps, + conditioning=c, + batch_size=batch_size, + shape=shape, + verbose=False, + unconditional_guidance_scale=scale, + unconditional_conditioning=uc, + eta=0.0, + dynamic_threshold=None, + ) + + samples = model.decode_first_stage(samples_latent) + samples = torch.clamp((samples+1.0)/2.0, min=0.0, max=1.0) + + all_samples.append(samples) + + all_samples = torch.cat(all_samples, 0) + return all_samples + + + @torch.inference_mode() + def __call__(self): + config = OmegaConf.load(self.cfg_path) + model, global_step = load_model_from_config(config, self.ckpt_path) + print(f"Restored model at global step {global_step}.") + + prompts = read_prompts(self.prompts_path) + + all_samples = self.sample(model, prompts, ema=True) + self.save_as_grid("grid_with_wings", all_samples, global_step) + all_samples = self.sample(model, prompts, ema=False) + self.save_as_grid("grid_without_wings", all_samples, global_step) + + + def save_as_grid(self, name, grid, global_step): + grid = make_grid(grid, nrow=self.nrow) + grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() + + os.makedirs(self.out_dir, exist_ok=True) + filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format( + name, + global_step, + 0, + 0, + ) + grid_path = os.path.join(self.out_dir, filename) + Image.fromarray(grid.astype(np.uint8)).save(grid_path) + print(f"---> {grid_path}") + + +class Checker(object): + def __init__(self, ckpt_path, callback, wait_for_file=5, interval=60): + self._cached_stamp = 0 + self.filename = ckpt_path + self.callback = callback + self.interval = interval + self.wait_for_file = wait_for_file + + def check(self): + while True: + stamp = os.stat(self.filename).st_mtime + if stamp != self._cached_stamp: + while True: + # try to wait until checkpoint is fully written + previous_stamp = stamp + time.sleep(self.wait_for_file) + stamp = os.stat(self.filename).st_mtime + if stamp != previous_stamp: + print(f"File is still changing. Waiting {self.wait_for_file} seconds.") + else: + break + + self._cached_stamp = stamp + # file has changed, so do something... + print(f"{self.__class__.__name__}: Detected a new file at " + f"{self.filename}, calling back.") + self.callback() + + else: + time.sleep(self.interval) + + +def run(prompts_path="scripts/prompts/prompts-with-wings.txt", + watch_log_dir=None, out_dir=None, ckpt_path=None, cfg_path=None, + H=256, + W=None, + C=4, + F=8, + wait_for_file=5, + interval=60): + + if out_dir is None: + assert watch_log_dir is not None + out_dir = os.path.join(watch_log_dir, "images/checker") + + if ckpt_path is None: + assert watch_log_dir is not None + ckpt_path = os.path.join(watch_log_dir, "checkpoints/last.ckpt") + + if cfg_path is None: + assert watch_log_dir is not None + configs = glob.glob(os.path.join(watch_log_dir, "configs/*-project.yaml")) + cfg_path = sorted(configs)[-1] + + if W is None: + assert H is not None + W = H + if H is None: + assert W is not None + H = W + shape = [C, H//F, W//F] + sampler = Sampler(out_dir, ckpt_path, cfg_path, prompts_path, shape=shape) + + checker = Checker(ckpt_path, sampler, wait_for_file=wait_for_file, interval=interval) + checker.check() + + +if __name__ == "__main__": + fire.Fire(run) diff --git a/scripts/slurm/v1_improvedaesthetics/launcher.sh b/scripts/slurm/v1_improvedaesthetics/launcher.sh new file mode 100755 index 0000000..9da9fc3 --- /dev/null +++ b/scripts/slurm/v1_improvedaesthetics/launcher.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# mpi version for node rank +H=`hostname` +THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"` +export NODE_RANK=${THEID} +echo THEID=$THEID + +echo "##########################################" +echo MASTER_ADDR=${MASTER_ADDR} +echo MASTER_PORT=${MASTER_PORT} +echo NODE_RANK=${NODE_RANK} +echo WORLD_SIZE=${WORLD_SIZE} +echo "##########################################" +# debug environment worked great so we stick with it +# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3 +# env with pip dependencies from stable diffusion's requirements.txt +eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)" +conda activate stable +cd /fsx/stable-diffusion/stable-diffusion + +CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_improvedaesthetics.yaml" + +# resume and set new seed to reshuffle data +EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt" + +# custom logdir +#EXTRA="${EXTRA} --logdir rlogs" + +# debugging +#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50" + +python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA diff --git a/scripts/slurm/v1_improvedaesthetics/sbatch.sh b/scripts/slurm/v1_improvedaesthetics/sbatch.sh new file mode 100755 index 0000000..768d103 --- /dev/null +++ b/scripts/slurm/v1_improvedaesthetics/sbatch.sh @@ -0,0 +1,42 @@ +#!/bin/bash +#SBATCH --partition=compute-od-gpu +#SBATCH --job-name=stable-diffusion-v1-improvedaesthetics +#SBATCH --nodes 20 +#SBATCH --ntasks-per-node 1 +#SBATCH --cpus-per-gpu=4 +#SBATCH --gres=gpu:8 +#SBATCH --exclusive +#SBATCH --output=%x_%j.out +#SBATCH --comment "Key=Monitoring,Value=ON" + +module load intelmpi +source /opt/intel/mpi/latest/env/vars.sh +export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst +all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH +export NCCL_PROTO=simple +export PATH=/opt/amazon/efa/bin:$PATH +export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so" +export FI_EFA_FORK_SAFE=1 +export FI_LOG_LEVEL=1 +export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn +export NCCL_DEBUG=info +export PYTHONFAULTHANDLER=1 +export CUDA_LAUNCH_BLOCKING=0 +export OMPI_MCA_mtl_base_verbose=1 +export FI_EFA_ENABLE_SHM_TRANSFER=0 +export FI_PROVIDER=efa +export FI_EFA_TX_MIN_CREDITS=64 +export NCCL_TREE_THRESHOLD=0 + +# sent to sub script +export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"` +export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) +export MASTER_PORT=12802 +export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l` +export WORLD_SIZE=$COUNT_NODE + +echo go $COUNT_NODE +echo $HOSTNAMES +echo $WORLD_SIZE + +mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_improvedaesthetics/launcher.sh diff --git a/scripts/slurm/v2_laionhr1024/launcher.sh b/scripts/slurm/v2_laionhr1024/launcher.sh new file mode 100755 index 0000000..a9be2cc --- /dev/null +++ b/scripts/slurm/v2_laionhr1024/launcher.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# mpi version for node rank +H=`hostname` +THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"` +export NODE_RANK=${THEID} +echo THEID=$THEID + +echo "##########################################" +echo MASTER_ADDR=${MASTER_ADDR} +echo MASTER_PORT=${MASTER_PORT} +echo NODE_RANK=${NODE_RANK} +echo WORLD_SIZE=${WORLD_SIZE} +echo "##########################################" +# debug environment worked great so we stick with it +# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3 +# env with pip dependencies from stable diffusion's requirements.txt +eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)" +conda activate stable +cd /fsx/stable-diffusion/stable-diffusion + +CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v2_laionhr1024.yaml" + +# resume and set new seed to reshuffle data +EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-12T00-50-44_txt2img-multinode-clip-encoder-f16-1024-laion-hr/checkpoints/last.ckpt" + +# custom logdir +#EXTRA="${EXTRA} --logdir rlogs" + +# debugging +#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50" + +python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA diff --git a/scripts/slurm/v2_laionhr1024/sbatch.sh b/scripts/slurm/v2_laionhr1024/sbatch.sh new file mode 100755 index 0000000..4e91b00 --- /dev/null +++ b/scripts/slurm/v2_laionhr1024/sbatch.sh @@ -0,0 +1,42 @@ +#!/bin/bash +#SBATCH --partition=compute-od-gpu +#SBATCH --job-name=stable-diffusion-v2-laionhr1024 +#SBATCH --nodes 20 +#SBATCH --ntasks-per-node 1 +#SBATCH --cpus-per-gpu=4 +#SBATCH --gres=gpu:8 +#SBATCH --exclusive +#SBATCH --output=%x_%j.out +#SBATCH --comment "Key=Monitoring,Value=ON" + +module load intelmpi +source /opt/intel/mpi/latest/env/vars.sh +export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst +all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH +export NCCL_PROTO=simple +export PATH=/opt/amazon/efa/bin:$PATH +export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so" +export FI_EFA_FORK_SAFE=1 +export FI_LOG_LEVEL=1 +export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn +export NCCL_DEBUG=info +export PYTHONFAULTHANDLER=1 +export CUDA_LAUNCH_BLOCKING=0 +export OMPI_MCA_mtl_base_verbose=1 +export FI_EFA_ENABLE_SHM_TRANSFER=0 +export FI_PROVIDER=efa +export FI_EFA_TX_MIN_CREDITS=64 +export NCCL_TREE_THRESHOLD=0 + +# sent to sub script +export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"` +export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) +export MASTER_PORT=12802 +export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l` +export WORLD_SIZE=$COUNT_NODE + +echo go $COUNT_NODE +echo $HOSTNAMES +echo $WORLD_SIZE + +mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v2_laionhr1024/launcher.sh diff --git a/scripts/slurm/v3_pretraining/launcher.sh b/scripts/slurm/v3_pretraining/launcher.sh new file mode 100755 index 0000000..6417636 --- /dev/null +++ b/scripts/slurm/v3_pretraining/launcher.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# mpi version for node rank +H=`hostname` +THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"` +export NODE_RANK=${THEID} +echo THEID=$THEID + +echo "##########################################" +echo MASTER_ADDR=${MASTER_ADDR} +echo MASTER_PORT=${MASTER_PORT} +echo NODE_RANK=${NODE_RANK} +echo WORLD_SIZE=${WORLD_SIZE} +echo "##########################################" +# debug environment worked great so we stick with it +# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3 +# env with pip dependencies from stable diffusion's requirements.txt +eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)" +conda activate stable +cd /fsx/stable-diffusion/stable-diffusion + +CONFIG=configs/stable-diffusion/v3_pretraining.yaml + +# resume and set new seed to reshuffle data +EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/rlogs/2022-07-11T22-57-10_txt2img-v2-clip-encoder-improved_aesthetics-256/checkpoints/last.ckpt" + +# custom logdir +#EXTRA="${EXTRA} --logdir rlogs" + +# debugging +#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50" + +python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA diff --git a/scripts/slurm/v3_pretraining/sbatch.sh b/scripts/slurm/v3_pretraining/sbatch.sh new file mode 100755 index 0000000..589c5c3 --- /dev/null +++ b/scripts/slurm/v3_pretraining/sbatch.sh @@ -0,0 +1,42 @@ +#!/bin/bash +#SBATCH --partition=compute-od-gpu +#SBATCH --job-name=stable-diffusion-v3-pretraining +#SBATCH --nodes 44 +#SBATCH --ntasks-per-node 1 +#SBATCH --cpus-per-gpu=4 +#SBATCH --gres=gpu:8 +#SBATCH --exclusive +#SBATCH --output=%x_%j.out +#SBATCH --comment "Key=Monitoring,Value=ON" + +module load intelmpi +source /opt/intel/mpi/latest/env/vars.sh +export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst +all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH +export NCCL_PROTO=simple +export PATH=/opt/amazon/efa/bin:$PATH +export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so" +export FI_EFA_FORK_SAFE=1 +export FI_LOG_LEVEL=1 +export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn +export NCCL_DEBUG=info +export PYTHONFAULTHANDLER=1 +export CUDA_LAUNCH_BLOCKING=0 +export OMPI_MCA_mtl_base_verbose=1 +export FI_EFA_ENABLE_SHM_TRANSFER=0 +export FI_PROVIDER=efa +export FI_EFA_TX_MIN_CREDITS=64 +export NCCL_TREE_THRESHOLD=0 + +# sent to sub script +export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"` +export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) +export MASTER_PORT=12802 +export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l` +export WORLD_SIZE=$COUNT_NODE + +echo go $COUNT_NODE +echo $HOSTNAMES +echo $WORLD_SIZE + +mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v3_pretraining/launcher.sh diff --git a/scripts/txt2img.py b/scripts/txt2img.py index cbf8525..6e98f83 100644 --- a/scripts/txt2img.py +++ b/scripts/txt2img.py @@ -40,7 +40,7 @@ def load_model_from_config(config, ckpt, verbose=False): return model -if __name__ == "__main__": +def main(): parser = argparse.ArgumentParser() parser.add_argument( @@ -258,3 +258,7 @@ if __name__ == "__main__": print(f"Your samples are ready and waiting for you here: \n{outpath} \n" f"Sampling took {toc-tic}s, i.e. produced {opt.n_iter * opt.n_samples / (toc - tic):.2f} samples/sec." f" \nEnjoy.") + + +if __name__ == "__main__": + main()