multinode hacks

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. ]

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()
 

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  ###

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()