Merge pull request #209 from damian0815/feat_rolling_save
Feature: Rolling save ckpt
This commit is contained in:
Victor Hall
GitHub
commit
6c8d15daab
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@ -0,0 +1,56 @@
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import math
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import os
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import shutil
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from plugins.plugins import BasePlugin
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from train import save_model
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EVERY_N_EPOCHS = 1 # how often to save. integers >= 1 save at the end of every nth epoch. floats < 1 subdivide the epoch evenly (eg 0.33 = 3 subdivisions)
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class InterruptiblePlugin(BasePlugin):
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def __init__(self):
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print("Interruptible plugin instantiated")
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self.previous_save_path = None
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self.every_n_epochs = EVERY_N_EPOCHS
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def on_epoch_start(self, **kwargs):
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epoch = kwargs['epoch']
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epoch_length = kwargs['epoch_length']
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self.steps_to_save_this_epoch = self._get_save_step_indices(epoch, epoch_length)
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def on_step_end(self, **kwargs):
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local_step = kwargs['local_step']
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if local_step in self.steps_to_save_this_epoch:
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global_step = kwargs['global_step']
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epoch = kwargs['epoch']
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project_name = kwargs['project_name']
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log_folder = kwargs['log_folder']
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ckpt_name = f"rolling-{project_name}-ep{epoch:02}-gs{global_step:05}"
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save_path = os.path.join(log_folder, "ckpts", ckpt_name)
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print(f"{type(self)} saving model to {save_path}")
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save_model(save_path, global_step=global_step, ed_state=kwargs['ed_state'], save_ckpt_dir=None, yaml_name=None, save_ckpt=False, save_full_precision=True, save_optimizer_flag=True)
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self._remove_previous()
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self.previous_save_path = save_path
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def on_training_end(self, **kwargs):
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self._remove_previous()
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def _remove_previous(self):
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if self.previous_save_path is not None:
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shutil.rmtree(self.previous_save_path, ignore_errors=True)
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self.previous_save_path = None
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def _get_save_step_indices(self, epoch, epoch_length_steps: int) -> list[int]:
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if self.every_n_epochs >= 1:
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if ((epoch+1) % self.every_n_epochs) == 0:
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# last step only
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return [epoch_length_steps-1]
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else:
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return []
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else:
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# subdivide the epoch evenly, by rounding self.every_n_epochs to the nearest clean division of steps
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num_divisions = max(1, min(epoch_length_steps, round(1/self.every_n_epochs)))
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# validation happens after training:
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# if an epoch has eg 100 steps and num_divisions is 2, then validation should occur after steps 49 and 99
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validate_every_n_steps = epoch_length_steps / num_divisions
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return [math.ceil((i+1)*validate_every_n_steps) - 1 for i in range(num_divisions)]
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@ -44,7 +44,7 @@ class Timer:
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def __exit__(self, type, value, traceback):
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elapsed_time = time.time() - self.start
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if elapsed_time > self.warn_seconds:
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logging.warning(f'Execution of {self.label} took {elapsed_time} seconds which is longer than the limit of {self.limit} seconds')
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logging.warning(f'Execution of {self.label} took {elapsed_time} seconds which is longer than the limit of {self.warn_seconds} seconds')
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class PluginRunner:
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288
train.py
288
train.py
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@ -27,6 +27,7 @@ import gc
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import random
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import traceback
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import shutil
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from typing import Optional
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import torch.nn.functional as F
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from torch.cuda.amp import autocast
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@ -102,6 +103,109 @@ def convert_to_hf(ckpt_path):
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is_sd1attn, yaml = get_attn_yaml(ckpt_path)
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return ckpt_path, is_sd1attn, yaml
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class EveryDreamTrainingState:
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def __init__(self,
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optimizer: EveryDreamOptimizer,
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train_batch: EveryDreamBatch,
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unet: UNet2DConditionModel,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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scheduler,
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vae: AutoencoderKL,
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unet_ema: Optional[UNet2DConditionModel],
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text_encoder_ema: Optional[CLIPTextModel]
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):
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self.optimizer = optimizer
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self.train_batch = train_batch
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self.unet = unet
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self.text_encoder = text_encoder
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self.tokenizer = tokenizer
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self.scheduler = scheduler
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self.vae = vae
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self.unet_ema = unet_ema
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self.text_encoder_ema = text_encoder_ema
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@torch.no_grad()
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def save_model(save_path, ed_state: EveryDreamTrainingState, global_step: int, save_ckpt_dir, yaml_name,
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save_full_precision=False, save_optimizer_flag=False, save_ckpt=True):
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"""
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Save the model to disk
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"""
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def save_ckpt_file(diffusers_model_path, sd_ckpt_path):
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nonlocal save_ckpt_dir
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nonlocal save_full_precision
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nonlocal yaml_name
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if save_ckpt_dir is not None:
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sd_ckpt_full = os.path.join(save_ckpt_dir, sd_ckpt_path)
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else:
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sd_ckpt_full = os.path.join(os.curdir, sd_ckpt_path)
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save_ckpt_dir = os.curdir
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half = not save_full_precision
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logging.info(f" * Saving SD model to {sd_ckpt_full}")
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converter(model_path=diffusers_model_path, checkpoint_path=sd_ckpt_full, half=half)
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if yaml_name and yaml_name != "v1-inference.yaml":
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yaml_save_path = f"{os.path.join(save_ckpt_dir, os.path.basename(diffusers_model_path))}.yaml"
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logging.info(f" * Saving yaml to {yaml_save_path}")
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shutil.copyfile(yaml_name, yaml_save_path)
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if global_step is None or global_step == 0:
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logging.warning(" No model to save, something likely blew up on startup, not saving")
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return
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if args.ema_decay_rate != None:
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pipeline_ema = StableDiffusionPipeline(
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vae=ed_state.vae,
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text_encoder=ed_state.text_encoder_ema,
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tokenizer=ed_state.tokenizer,
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unet=ed_state.unet_ema,
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scheduler=ed_state.scheduler,
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safety_checker=None, # save vram
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requires_safety_checker=None, # avoid nag
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feature_extractor=None, # must be none of no safety checker
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)
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diffusers_model_path = save_path + "_ema"
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logging.info(f" * Saving diffusers EMA model to {diffusers_model_path}")
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pipeline_ema.save_pretrained(diffusers_model_path)
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if save_ckpt:
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sd_ckpt_path_ema = f"{os.path.basename(save_path)}_ema.ckpt"
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save_ckpt_file(diffusers_model_path, sd_ckpt_path_ema)
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pipeline = StableDiffusionPipeline(
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vae=ed_state.vae,
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text_encoder=ed_state.text_encoder,
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tokenizer=ed_state.tokenizer,
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unet=ed_state.unet,
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scheduler=ed_state.scheduler,
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safety_checker=None, # save vram
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requires_safety_checker=None, # avoid nag
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feature_extractor=None, # must be none of no safety checker
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)
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diffusers_model_path = save_path
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logging.info(f" * Saving diffusers model to {diffusers_model_path}")
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pipeline.save_pretrained(diffusers_model_path)
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if save_ckpt:
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sd_ckpt_path = f"{os.path.basename(save_path)}.ckpt"
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save_ckpt_file(diffusers_model_path, sd_ckpt_path)
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if save_optimizer_flag:
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logging.info(f" Saving optimizer state to {save_path}")
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ed_state.optimizer.save(save_path)
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def setup_local_logger(args):
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"""
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configures logger with file and console logging, logs args, and returns the datestamp
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@ -477,95 +581,6 @@ def main(args):
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if 'cuda' in original_device.type:
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torch.cuda.empty_cache()
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@torch.no_grad()
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def __save_model(save_path, tokenizer, scheduler, vae, ed_optimizer, save_ckpt_dir, yaml_name,
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save_full_precision=False, save_optimizer_flag=False, save_ckpt=True):
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nonlocal unet
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nonlocal text_encoder
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nonlocal unet_ema
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nonlocal text_encoder_ema
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"""
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Save the model to disk
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"""
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def save_ckpt_file(diffusers_model_path, sd_ckpt_path):
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nonlocal save_ckpt_dir
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nonlocal save_full_precision
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nonlocal yaml_name
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if save_ckpt_dir is not None:
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sd_ckpt_full = os.path.join(save_ckpt_dir, sd_ckpt_path)
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else:
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sd_ckpt_full = os.path.join(os.curdir, sd_ckpt_path)
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save_ckpt_dir = os.curdir
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half = not save_full_precision
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logging.info(f" * Saving SD model to {sd_ckpt_full}")
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converter(model_path=diffusers_model_path, checkpoint_path=sd_ckpt_full, half=half)
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if yaml_name and yaml_name != "v1-inference.yaml":
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yaml_save_path = f"{os.path.join(save_ckpt_dir, os.path.basename(diffusers_model_path))}.yaml"
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logging.info(f" * Saving yaml to {yaml_save_path}")
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shutil.copyfile(yaml_name, yaml_save_path)
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global global_step
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if global_step is None or global_step == 0:
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logging.warning(" No model to save, something likely blew up on startup, not saving")
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return
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if args.ema_decay_rate != None:
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pipeline_ema = StableDiffusionPipeline(
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vae=vae,
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text_encoder=text_encoder_ema,
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tokenizer=tokenizer,
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unet=unet_ema,
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scheduler=scheduler,
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safety_checker=None, # save vram
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requires_safety_checker=None, # avoid nag
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feature_extractor=None, # must be none of no safety checker
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)
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diffusers_model_path = save_path + "_ema"
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logging.info(f" * Saving diffusers EMA model to {diffusers_model_path}")
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pipeline_ema.save_pretrained(diffusers_model_path)
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if save_ckpt:
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sd_ckpt_path_ema = f"{os.path.basename(save_path)}_ema.ckpt"
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save_ckpt_file(diffusers_model_path, sd_ckpt_path_ema)
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pipeline = StableDiffusionPipeline(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=None, # save vram
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requires_safety_checker=None, # avoid nag
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feature_extractor=None, # must be none of no safety checker
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)
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diffusers_model_path = save_path
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logging.info(f" * Saving diffusers model to {diffusers_model_path}")
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pipeline.save_pretrained(diffusers_model_path)
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if save_ckpt:
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sd_ckpt_path = f"{os.path.basename(save_path)}.ckpt"
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save_ckpt_file(diffusers_model_path, sd_ckpt_path)
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if save_optimizer_flag:
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logging.info(f" Saving optimizer state to {save_path}")
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ed_optimizer.save(save_path)
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use_ema_dacay_training = (args.ema_decay_rate != None) or (args.ema_strength_target != None)
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ema_model_loaded_from_file = False
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@ -574,6 +589,7 @@ def main(args):
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ema_device = torch.device(args.ema_device)
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optimizer_state_path = None
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try:
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# check for a local file
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hf_cache_path = get_hf_ckpt_cache_path(args.resume_ckpt)
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@ -582,10 +598,6 @@ def main(args):
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text_encoder = CLIPTextModel.from_pretrained(model_root_folder, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(model_root_folder, subfolder="vae")
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unet = UNet2DConditionModel.from_pretrained(model_root_folder, subfolder="unet")
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optimizer_state_path = os.path.join(args.resume_ckpt, "optimizer.pt")
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if not os.path.exists(optimizer_state_path):
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optimizer_state_path = None
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else:
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# try to download from HF using resume_ckpt as a repo id
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downloaded = try_download_model_from_hf(repo_id=args.resume_ckpt)
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@ -700,7 +712,9 @@ def main(args):
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# Make sure correct types are used for models
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unet_ema = unet_ema.to(ema_device, dtype=unet.dtype)
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text_encoder_ema = text_encoder_ema.to(ema_device, dtype=text_encoder.dtype)
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else:
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unet_ema = None
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text_encoder_ema = None
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try:
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#unet = torch.compile(unet)
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@ -834,9 +848,9 @@ def main(args):
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logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, attempting to save model to {interrupted_checkpoint_path}{Style.RESET_ALL}")
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logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
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time.sleep(2) # give opportunity to ctrl-C again to cancel save
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__save_model(interrupted_checkpoint_path, tokenizer, noise_scheduler, vae,
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ed_optimizer, args.save_ckpt_dir, args.save_full_precision, args.save_optimizer,
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save_ckpt=not args.no_save_ckpt)
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save_model(interrupted_checkpoint_path, global_step=global_step, ed_state=make_current_ed_state(),
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save_ckpt_dir=args.save_ckpt_dir, yaml_name=yaml, save_full_precision=args.save_full_precision,
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save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
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exit(_SIGTERM_EXIT_CODE)
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else:
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# non-main threads (i.e. dataloader workers) should exit cleanly
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@ -1031,7 +1045,12 @@ def main(args):
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torch.cuda.empty_cache()
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def make_save_path(epoch, global_step, prepend=""):
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return os.path.join(f"{log_folder}/ckpts/{prepend}{args.project_name}-ep{epoch:02}-gs{global_step:05}")
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basename = f"{prepend}{args.project_name}"
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if epoch is not None:
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basename += f"-ep{epoch:02}"
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if global_step is not None:
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basename += f"-gs{global_step:05}"
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return os.path.join(log_folder, "ckpts", basename)
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# Pre-train validation to establish a starting point on the loss graph
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@ -1054,25 +1073,43 @@ def main(args):
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from plugins.plugins import PluginRunner
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plugin_runner = PluginRunner(plugins=plugins)
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def make_current_ed_state() -> EveryDreamTrainingState:
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return EveryDreamTrainingState(optimizer=ed_optimizer,
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train_batch=train_batch,
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unet=unet,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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scheduler=noise_scheduler,
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vae=vae,
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unet_ema=unet_ema,
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text_encoder_ema=text_encoder_ema)
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epoch = None
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try:
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write_batch_schedule(args, log_folder, train_batch, epoch = 0)
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plugin_runner.run_on_training_start(log_folder=log_folder, project_name=args.project_name)
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for epoch in range(args.max_epochs):
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if args.load_settings_every_epoch:
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load_train_json_from_file(args)
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plugin_runner.run_on_epoch_start(epoch=epoch,
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global_step=global_step,
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project_name=args.project_name,
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log_folder=log_folder,
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data_root=args.data_root)
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epoch_len = math.ceil(len(train_batch) / args.batch_size)
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plugin_runner.run_on_epoch_start(
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epoch=epoch,
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global_step=global_step,
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epoch_length=epoch_len,
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project_name=args.project_name,
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log_folder=log_folder,
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data_root=args.data_root
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)
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loss_epoch = []
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epoch_start_time = time.time()
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images_per_sec_log_step = []
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epoch_len = math.ceil(len(train_batch) / args.batch_size)
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steps_pbar = tqdm(range(epoch_len), position=1, leave=False, dynamic_ncols=True)
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steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{Style.RESET_ALL}")
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@ -1082,13 +1119,16 @@ def main(args):
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)
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for step, batch in enumerate(train_dataloader):
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step_start_time = time.time()
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plugin_runner.run_on_step_start(epoch=epoch,
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local_step=step,
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global_step=global_step,
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project_name=args.project_name,
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log_folder=log_folder,
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batch=batch)
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batch=batch,
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ed_state=make_current_ed_state())
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model_pred, target, loss = get_model_prediction_and_target(batch["image"], batch["tokens"], args.zero_frequency_noise_ratio, return_loss=True)
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@ -1155,27 +1195,29 @@ def main(args):
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min_since_last_ckpt = (time.time() - last_epoch_saved_time) / 60
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needs_save = False
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if args.ckpt_every_n_minutes is not None and (min_since_last_ckpt > args.ckpt_every_n_minutes):
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last_epoch_saved_time = time.time()
|
||||
logging.info(f"Saving model, {args.ckpt_every_n_minutes} mins at step {global_step}")
|
||||
save_path = make_save_path(epoch, global_step)
|
||||
__save_model(save_path, tokenizer, noise_scheduler, vae, ed_optimizer,
|
||||
args.save_ckpt_dir, yaml, args.save_full_precision, args.save_optimizer,
|
||||
save_ckpt=not args.no_save_ckpt)
|
||||
|
||||
needs_save = True
|
||||
if epoch > 0 and epoch % args.save_every_n_epochs == 0 and step == 0 and epoch < args.max_epochs - 1 and epoch >= args.save_ckpts_from_n_epochs:
|
||||
logging.info(f" Saving model, {args.save_every_n_epochs} epochs at step {global_step}")
|
||||
needs_save = True
|
||||
if needs_save:
|
||||
save_path = make_save_path(epoch, global_step)
|
||||
__save_model(save_path, tokenizer, noise_scheduler, vae, ed_optimizer,
|
||||
args.save_ckpt_dir, yaml, args.save_full_precision, args.save_optimizer,
|
||||
save_ckpt=not args.no_save_ckpt)
|
||||
save_model(save_path, global_step=global_step, ed_state=make_current_ed_state(),
|
||||
save_ckpt_dir=None, yaml_name=None,
|
||||
save_full_precision=args.save_full_precision,
|
||||
save_optimizer_flag=args.save_optimizer, save_ckpt=False)
|
||||
|
||||
plugin_runner.run_on_step_end(epoch=epoch,
|
||||
global_step=global_step,
|
||||
local_step=step,
|
||||
project_name=args.project_name,
|
||||
log_folder=log_folder,
|
||||
data_root=args.data_root,
|
||||
batch=batch)
|
||||
batch=batch,
|
||||
ed_state=make_current_ed_state())
|
||||
|
||||
del batch
|
||||
global_step += 1
|
||||
|
|
@ -1192,8 +1234,9 @@ def main(args):
|
|||
train_batch.shuffle(epoch_n=epoch, max_epochs = args.max_epochs)
|
||||
write_batch_schedule(args, log_folder, train_batch, epoch + 1)
|
||||
|
||||
loss_epoch = sum(loss_epoch) / len(loss_epoch)
|
||||
log_writer.add_scalar(tag="loss/epoch", scalar_value=loss_epoch, global_step=global_step)
|
||||
if len(loss_epoch) > 0:
|
||||
loss_epoch = sum(loss_epoch) / len(loss_epoch)
|
||||
log_writer.add_scalar(tag="loss/epoch", scalar_value=loss_epoch, global_step=global_step)
|
||||
|
||||
plugin_runner.run_on_epoch_end(epoch=epoch,
|
||||
global_step=global_step,
|
||||
|
|
@ -1206,9 +1249,13 @@ def main(args):
|
|||
|
||||
# end of training
|
||||
epoch = args.max_epochs
|
||||
|
||||
plugin_runner.run_on_training_end()
|
||||
|
||||
save_path = make_save_path(epoch, global_step, prepend=("" if args.no_prepend_last else "last-"))
|
||||
__save_model(save_path, tokenizer, noise_scheduler, vae, ed_optimizer, args.save_ckpt_dir,
|
||||
yaml, args.save_full_precision, args.save_optimizer, save_ckpt=not args.no_save_ckpt)
|
||||
save_model(save_path, global_step=global_step, ed_state=make_current_ed_state(),
|
||||
save_ckpt_dir=args.save_ckpt_dir, yaml_name=yaml, save_full_precision=args.save_full_precision,
|
||||
save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
|
||||
|
||||
total_elapsed_time = time.time() - training_start_time
|
||||
logging.info(f"{Fore.CYAN}Training complete{Style.RESET_ALL}")
|
||||
|
|
@ -1218,8 +1265,9 @@ def main(args):
|
|||
except Exception as ex:
|
||||
logging.error(f"{Fore.LIGHTYELLOW_EX}Something went wrong, attempting to save model{Style.RESET_ALL}")
|
||||
save_path = make_save_path(epoch, global_step, prepend="errored-")
|
||||
__save_model(save_path, tokenizer, noise_scheduler, vae, ed_optimizer, args.save_ckpt_dir,
|
||||
yaml, args.save_full_precision, args.save_optimizer, save_ckpt=not args.no_save_ckpt)
|
||||
save_model(save_path, global_step=global_step, ed_state=make_current_ed_state(),
|
||||
save_ckpt_dir=args.save_ckpt_dir, yaml_name=yaml, save_full_precision=args.save_full_precision,
|
||||
save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
|
||||
logging.info(f"{Fore.LIGHTYELLOW_EX}Model saved, re-raising exception and exiting. Exception was:{Style.RESET_ALL}{Fore.LIGHTRED_EX} {ex} {Style.RESET_ALL}")
|
||||
raise ex
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue