1510 lines
68 KiB
Python
1510 lines
68 KiB
Python
"""Causal-Forcing Stage 1 (Autoregressive Diffusion) training, modified from
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scripts/wan2.1_self_forcing/train_distill.py and Causal-Forcing
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(https://github.com/thu-ml/Causal-Forcing).
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"""
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import argparse
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import gc
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import logging
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import math
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import os
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import pickle
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import shutil
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import sys
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import accelerate
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import diffusers
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.state import AcceleratorState
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from accelerate.utils import ProjectConfiguration, set_seed
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from diffusers import FlowMatchEulerDiscreteScheduler
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from diffusers.optimization import get_scheduler
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from diffusers.utils import check_min_version, is_wandb_available
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from diffusers.utils.torch_utils import is_compiled_module
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from einops import rearrange
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from omegaconf import OmegaConf
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from packaging import version
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import AutoTokenizer
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from transformers.utils import ContextManagers
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import datasets
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current_file_path = os.path.abspath(__file__)
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project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.data import (ASPECT_RATIO_512, ASPECT_RATIO_RANDOM_CROP_512,
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ASPECT_RATIO_RANDOM_CROP_PROB,
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AspectRatioBatchImageVideoSampler,
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ImageVideoDataset, RandomSampler,
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get_closest_ratio)
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from videox_fun.models import (AutoencoderKLWan, WanT5EncoderModel,
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WanTransformer3DModel_SelfForcing)
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from videox_fun.pipeline import WanSelfForcingPipeline
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from videox_fun.utils.utils import save_videos_grid
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if is_wandb_available():
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import wandb
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def filter_kwargs(cls, kwargs):
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import inspect
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sig = inspect.signature(cls.__init__)
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valid_params = set(sig.parameters.keys()) - {'self', 'cls'}
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filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params}
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return filtered_kwargs
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def get_random_downsample_ratio(sample_size, image_ratio=[],
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all_choices=False, rng=None):
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def _create_special_list(length):
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if length == 1:
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return [1.0]
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if length >= 2:
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first_element = 0.75
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remaining_sum = 1.0 - first_element
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other_elements_value = remaining_sum / (length - 1)
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special_list = [first_element] + [other_elements_value] * (length - 1)
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return special_list
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if sample_size >= 1536:
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number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio
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elif sample_size >= 1024:
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number_list = [1, 1.25, 1.5, 2] + image_ratio
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elif sample_size >= 768:
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number_list = [1, 1.25, 1.5] + image_ratio
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elif sample_size >= 512:
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number_list = [1] + image_ratio
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else:
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number_list = [1]
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if all_choices:
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return number_list
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number_list_prob = np.array(_create_special_list(len(number_list)))
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if rng is None:
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return np.random.choice(number_list, p = number_list_prob)
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else:
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return rng.choice(number_list, p = number_list_prob)
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.18.0.dev0")
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logger = get_logger(__name__, log_level="INFO")
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def log_validation(vae, text_encoder, tokenizer, transformer3d, args, config, accelerator, weight_dtype, global_step):
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try:
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is_deepspeed = type(transformer3d).__name__ == 'DeepSpeedEngine'
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if is_deepspeed:
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origin_config = transformer3d.config
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transformer3d.config = accelerator.unwrap_model(transformer3d).config
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with torch.no_grad(), torch.amp.autocast('cuda', dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
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logger.info("Running validation... ")
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scheduler_kwargs = OmegaConf.to_container(config['scheduler_kwargs'])
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scheduler_kwargs['shift'] = args.shift
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scheduler = FlowMatchEulerDiscreteScheduler(
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**filter_kwargs(FlowMatchEulerDiscreteScheduler, scheduler_kwargs)
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)
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pipeline = WanSelfForcingPipeline(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=accelerator.unwrap_model(transformer3d) if type(transformer3d).__name__ == 'DistributedDataParallel' else transformer3d,
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scheduler=scheduler,
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)
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pipeline = pipeline.to(accelerator.device)
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if args.seed is None:
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generator = None
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else:
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rank_seed = args.seed + accelerator.process_index
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generator = torch.Generator(device=accelerator.device).manual_seed(rank_seed)
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logger.info(f"Rank {accelerator.process_index} using seed: {rank_seed}")
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for i in range(len(args.validation_prompts)):
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if args.fix_sample_size is not None:
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height, width = args.fix_sample_size
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else:
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height, width = args.video_sample_size, args.video_sample_size
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sample = pipeline(
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args.validation_prompts[i],
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num_frames = args.video_sample_n_frames,
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negative_prompt = args.negative_prompt,
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height = height,
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width = width,
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generator = generator,
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guidance_scale = args.validation_guidance_scale,
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num_inference_steps = args.validation_num_inference_steps,
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shift = args.shift,
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num_frame_per_block = args.num_frame_per_block,
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independent_first_frame = args.independent_first_frame,
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context_noise = 0,
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stochastic_sampling = False,
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).videos
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os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
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save_videos_grid(
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sample,
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os.path.join(
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args.output_dir,
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f"sample/sample-{global_step}-rank{accelerator.process_index}-image-{i}.mp4"
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)
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)
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del pipeline
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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if not args.enable_text_encoder_in_dataloader:
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text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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if is_deepspeed:
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transformer3d.config = origin_config
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except Exception as e:
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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print(f"Eval error on rank {accelerator.process_index} with info {e}")
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vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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if not args.enable_text_encoder_in_dataloader:
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text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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def parse_args():
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parser = argparse.ArgumentParser(description="Causal-Forcing Stage 1: Autoregressive Diffusion training for Wan2.1.")
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. "
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),
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)
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parser.add_argument(
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"--train_data_meta",
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type=str,
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default=None,
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help=(
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"A csv containing the training data. "
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),
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)
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parser.add_argument(
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"--validation_prompts",
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type=str,
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default=None,
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nargs="+",
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help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--negative_prompt",
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type=str,
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default="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
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help=("The negative prompt used for validation generation."),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="sd-model-finetuned",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--use_came",
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action="store_true",
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help="whether to use came",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--vae_mini_batch", type=int, default=32, help="mini batch size for vae."
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)
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parser.add_argument("--num_train_epochs", type=int, default=100)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-4,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument(
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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="tensorboard",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
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' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
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),
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)
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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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parser.add_argument(
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"--checkpointing_steps",
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type=int,
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default=500,
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help=(
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"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
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" training using `--resume_from_checkpoint`."
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),
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)
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parser.add_argument(
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"--checkpoints_total_limit",
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type=int,
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default=None,
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help=("Max number of checkpoints to store."),
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help=(
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"Whether training should be resumed from a previous checkpoint. Use a path saved by"
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' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
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),
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)
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parser.add_argument(
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"--validation_epochs",
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type=int,
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default=5,
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help="Run validation every X epochs.",
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)
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parser.add_argument(
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"--validation_steps",
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type=int,
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default=2000,
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help="Run validation every X steps.",
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)
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parser.add_argument(
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"--validation_guidance_scale",
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type=float,
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default=3.0,
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help="CFG scale used when sampling validation videos.",
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)
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parser.add_argument(
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"--validation_num_inference_steps",
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type=int,
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default=50,
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help=(
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"Number of denoising steps used for validation. AR diffusion validation runs a"
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" full multi-step rollout, so a larger value (e.g. 50) is recommended."
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),
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)
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|
parser.add_argument(
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"--tracker_project_name",
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type=str,
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default="text2image-fine-tune",
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|
help=(
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|
"The `project_name` argument passed to Accelerator.init_trackers for"
|
|
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
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),
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)
|
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|
parser.add_argument(
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"--enable_text_encoder_in_dataloader", action="store_true", help="Whether or not to use text encoder in dataloader."
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|
)
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|
parser.add_argument(
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|
"--enable_bucket", action="store_true", help="Whether enable bucket sample in datasets."
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|
)
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|
parser.add_argument(
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|
"--random_ratio_crop", action="store_true", help="Whether enable random ratio crop sample in datasets."
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)
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|
parser.add_argument(
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|
"--random_hw_adapt", action="store_true", help="Whether enable random adapt height and width in datasets."
|
|
)
|
|
parser.add_argument(
|
|
"--training_with_video_token_length", action="store_true", help="The training stage of the model in training.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_sampling_steps",
|
|
type=int,
|
|
default=1000,
|
|
help="Total number of scheduler timesteps for sampling.",
|
|
)
|
|
parser.add_argument(
|
|
"--token_sample_size",
|
|
type=int,
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|
default=512,
|
|
help="Sample size of the token.",
|
|
)
|
|
parser.add_argument(
|
|
"--video_sample_size",
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|
type=int,
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default=512,
|
|
help="Sample size of the video.",
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)
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|
parser.add_argument(
|
|
"--image_sample_size",
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|
type=int,
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|
default=512,
|
|
help="Sample size of the image.",
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|
)
|
|
parser.add_argument(
|
|
"--fix_sample_size",
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|
nargs=2, type=int, default=None,
|
|
help="Fix Sample size [height, width] when using bucket and collate_fn."
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|
)
|
|
parser.add_argument(
|
|
"--video_sample_stride",
|
|
type=int,
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|
default=4,
|
|
help="Sample stride of the video.",
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|
)
|
|
parser.add_argument(
|
|
"--video_sample_n_frames",
|
|
type=int,
|
|
default=17,
|
|
help="Num frame of video.",
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|
)
|
|
parser.add_argument(
|
|
"--video_repeat",
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|
type=int,
|
|
default=0,
|
|
help="Num of repeat video.",
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|
)
|
|
parser.add_argument(
|
|
"--config_path",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"The config of the model in training."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--transformer_path",
|
|
type=str,
|
|
default=None,
|
|
help=("If you want to load the weight from other transformers, input its path."),
|
|
)
|
|
parser.add_argument(
|
|
"--vae_path",
|
|
type=str,
|
|
default=None,
|
|
help=("If you want to load the weight from other vaes, input its path."),
|
|
)
|
|
|
|
parser.add_argument(
|
|
'--trainable_modules',
|
|
nargs='+',
|
|
help='Enter a list of trainable modules'
|
|
)
|
|
parser.add_argument(
|
|
'--trainable_modules_low_learning_rate',
|
|
nargs='+',
|
|
default=[],
|
|
help='Enter a list of trainable modules with lower learning rate'
|
|
)
|
|
parser.add_argument(
|
|
'--tokenizer_max_length',
|
|
type=int,
|
|
default=512,
|
|
help='Max length of tokenizer'
|
|
)
|
|
parser.add_argument(
|
|
"--use_deepspeed", action="store_true", help="Whether or not to use deepspeed."
|
|
)
|
|
parser.add_argument(
|
|
"--use_fsdp", action="store_true", help="Whether or not to use fsdp."
|
|
)
|
|
parser.add_argument(
|
|
"--low_vram", action="store_true", help="Whether enable low_vram mode."
|
|
)
|
|
parser.add_argument(
|
|
"--num_frame_per_block",
|
|
type=int,
|
|
default=3,
|
|
help="Number of latent frames per causal block. 3 = chunk-wise, 1 = frame-wise."
|
|
)
|
|
parser.add_argument(
|
|
"--independent_first_frame",
|
|
action="store_true",
|
|
help="Whether first frame is independent ([1, N, N, ...] pattern, useful for I2V)."
|
|
)
|
|
parser.add_argument(
|
|
"--shift",
|
|
type=float,
|
|
default=5.0,
|
|
help="Shift value for FlowMatchEulerDiscreteScheduler. Causal-Forcing uses 5.0 by default."
|
|
)
|
|
parser.add_argument(
|
|
"--no_teacher_forcing",
|
|
action="store_true",
|
|
help="Disable teacher forcing (train under diffusion forcing instead). Stage 1 defaults to TF."
|
|
)
|
|
parser.add_argument(
|
|
"--noise_augmentation_max_timestep",
|
|
type=int,
|
|
default=0,
|
|
help=(
|
|
"If > 0, add light flow-matching noise (sampled in [0, value)) "
|
|
"to the clean context tokens during teacher forcing."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--use_timestep_weight",
|
|
action="store_true",
|
|
help="Apply the Causal-Forcing per-timestep loss weight (Gaussian centered at T/2)."
|
|
)
|
|
args = parser.parse_args()
|
|
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
|
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
|
args.local_rank = env_local_rank
|
|
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
|
|
logging_dir = os.path.join(args.output_dir, args.logging_dir)
|
|
|
|
config = OmegaConf.load(args.config_path)
|
|
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
)
|
|
|
|
deepspeed_plugin = accelerator.state.deepspeed_plugin if hasattr(accelerator.state, "deepspeed_plugin") else None
|
|
fsdp_plugin = accelerator.state.fsdp_plugin if hasattr(accelerator.state, "fsdp_plugin") else None
|
|
if deepspeed_plugin is not None:
|
|
zero_stage = int(deepspeed_plugin.zero_stage)
|
|
fsdp_stage = 0
|
|
print(f"Using DeepSpeed Zero stage: {zero_stage}")
|
|
|
|
args.use_deepspeed = True
|
|
if zero_stage == 3:
|
|
print(f"Auto set save_state to True because zero_stage == 3")
|
|
args.save_state = True
|
|
elif fsdp_plugin is not None:
|
|
from torch.distributed.fsdp import ShardingStrategy
|
|
zero_stage = 0
|
|
if fsdp_plugin.sharding_strategy is ShardingStrategy.FULL_SHARD:
|
|
fsdp_stage = 3
|
|
elif fsdp_plugin.sharding_strategy is None: # The fsdp_plugin.sharding_strategy is None in FSDP 2.
|
|
fsdp_stage = 3
|
|
elif fsdp_plugin.sharding_strategy is ShardingStrategy.SHARD_GRAD_OP:
|
|
fsdp_stage = 2
|
|
else:
|
|
fsdp_stage = 0
|
|
print(f"Using FSDP stage: {fsdp_stage}")
|
|
|
|
args.use_fsdp = True
|
|
if fsdp_stage == 3:
|
|
print(f"Auto set save_state to True because fsdp_stage == 3")
|
|
args.save_state = True
|
|
else:
|
|
zero_stage = 0
|
|
fsdp_stage = 0
|
|
print("DeepSpeed is not enabled.")
|
|
|
|
if accelerator.is_main_process:
|
|
writer = SummaryWriter(log_dir=logging_dir)
|
|
|
|
# Make one log on every process with the configuration for debugging.
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
)
|
|
logger.info(accelerator.state, main_process_only=False)
|
|
if accelerator.is_local_main_process:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
diffusers.utils.logging.set_verbosity_error()
|
|
|
|
# If passed along, set the training seed now.
|
|
if args.seed is not None:
|
|
set_seed(args.seed)
|
|
rng = np.random.default_rng(np.random.PCG64(args.seed + accelerator.process_index))
|
|
torch_rng = torch.Generator(accelerator.device).manual_seed(args.seed + accelerator.process_index)
|
|
print(f"Init rng with seed {args.seed + accelerator.process_index}. Process_index is {accelerator.process_index}")
|
|
else:
|
|
rng = None
|
|
torch_rng = None
|
|
print(f"No seed provided; using global default RNG. Process_index is {accelerator.process_index}")
|
|
|
|
# Handle the repository creation
|
|
if accelerator.is_main_process:
|
|
if args.output_dir is not None:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora transformer3d) to half-precision
|
|
# as these weights are only used for inference, keeping weights in full precision is not required.
|
|
weight_dtype = torch.float32
|
|
if accelerator.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
args.mixed_precision = accelerator.mixed_precision
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
args.mixed_precision = accelerator.mixed_precision
|
|
|
|
# Load scheduler, tokenizer and models.
|
|
scheduler_kwargs = OmegaConf.to_container(config['scheduler_kwargs'])
|
|
scheduler_kwargs['shift'] = args.shift
|
|
noise_scheduler = FlowMatchEulerDiscreteScheduler(
|
|
**filter_kwargs(FlowMatchEulerDiscreteScheduler, scheduler_kwargs)
|
|
)
|
|
|
|
# Get Tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, config['text_encoder_kwargs'].get('tokenizer_subpath', 'tokenizer')),
|
|
)
|
|
|
|
def deepspeed_zero_init_disabled_context_manager():
|
|
"""
|
|
returns either a context list that includes one that will disable zero.Init or an empty context list
|
|
"""
|
|
deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
|
|
if deepspeed_plugin is None:
|
|
return []
|
|
|
|
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
|
|
|
|
# Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
|
|
# For this to work properly all models must be run through `accelerate.prepare`. But accelerate
|
|
# will try to assign the same optimizer with the same weights to all models during
|
|
# `deepspeed.initialize`, which of course doesn't work.
|
|
#
|
|
# For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
|
|
# frozen models from being partitioned during `zero.Init` which gets called during
|
|
# `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
|
|
# across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
|
|
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
|
|
# Get Text encoder
|
|
text_encoder = WanT5EncoderModel.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, config['text_encoder_kwargs'].get('text_encoder_subpath', 'text_encoder')),
|
|
additional_kwargs=OmegaConf.to_container(config['text_encoder_kwargs']),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
text_encoder = text_encoder.eval()
|
|
# Get Vae
|
|
vae = AutoencoderKLWan.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, config['vae_kwargs'].get('vae_subpath', 'vae')),
|
|
additional_kwargs=OmegaConf.to_container(config['vae_kwargs']),
|
|
)
|
|
vae.eval()
|
|
|
|
# Get Transformer (causal generator).
|
|
# IMPORTANT: keep the trainable transformer in fp32. accelerate's
|
|
# mixed_precision="bf16" will autocast the forward to bf16 while keeping
|
|
# the master weights and Adam moments in fp32. If params live in bf16,
|
|
# every update (LR*grad ~ 1e-5 for LR=2e-6) falls below bf16 mantissa
|
|
# precision (~1e-3 relative) and is rounded to zero, so the model barely
|
|
# moves over thousands of steps and never learns the causal/KV-cache
|
|
# structure. CF stage 1 also keeps params in fp32 (its 10k ckpt on disk
|
|
# is fp32; with the bug above ours used to be bf16).
|
|
#
|
|
# NOTE: WanTransformer3DModel.from_pretrained defaults to torch_dtype=bf16
|
|
# and ends with `model = model.to(torch_dtype)`, so an explicit
|
|
# torch_dtype=torch.float32 is required to keep the loaded weights fp32.
|
|
transformer3d = WanTransformer3DModel_SelfForcing.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, config['transformer_additional_kwargs'].get('transformer_subpath', 'transformer')),
|
|
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=torch.float32,
|
|
)
|
|
# Stage 1 only trains the causal generator; mark the block layout the
|
|
# downstream Self-Forcing pipeline expects at sampling time.
|
|
transformer3d.num_frame_per_block = args.num_frame_per_block
|
|
transformer3d.independent_first_frame = args.independent_first_frame
|
|
|
|
# Freeze vae and text_encoder; transformer3d is toggled per-module below.
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
transformer3d.requires_grad_(False)
|
|
|
|
if args.transformer_path is not None:
|
|
print(f"From checkpoint: {args.transformer_path}")
|
|
if args.transformer_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(args.transformer_path)
|
|
else:
|
|
state_dict = torch.load(args.transformer_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
state_dict = state_dict["generator_ema"] if "generator_ema" in state_dict else state_dict
|
|
state_dict = state_dict["generator"] if "generator" in state_dict else state_dict
|
|
if any(k.startswith("model.") for k in state_dict.keys()):
|
|
state_dict = {k.replace("model.", "", 1) if k.startswith("model.") else k: v for k, v in state_dict.items()}
|
|
|
|
m, u = transformer3d.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
assert len(u) == 0
|
|
|
|
if args.vae_path is not None:
|
|
print(f"From checkpoint: {args.vae_path}")
|
|
if args.vae_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(args.vae_path)
|
|
else:
|
|
state_dict = torch.load(args.vae_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
|
|
m, u = vae.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
assert len(u) == 0
|
|
|
|
# A good trainable modules is showed below now.
|
|
# For 3D Patch: trainable_modules = ['ff.net', 'pos_embed', 'attn2', 'proj_out', 'timepositionalencoding', 'h_position', 'w_position']
|
|
# For 2D Patch: trainable_modules = ['ff.net', 'attn2', 'timepositionalencoding', 'h_position', 'w_position']
|
|
transformer3d.train()
|
|
if accelerator.is_main_process:
|
|
accelerator.print(
|
|
f"Trainable modules '{args.trainable_modules}'."
|
|
)
|
|
for name, param in transformer3d.named_parameters():
|
|
for trainable_module_name in args.trainable_modules + args.trainable_modules_low_learning_rate:
|
|
if trainable_module_name in name:
|
|
param.requires_grad = True
|
|
break
|
|
|
|
# `accelerate` 0.16.0 will have better support for customized saving
|
|
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
if fsdp_stage != 0 or zero_stage == 3:
|
|
def save_model_hook(models, weights, output_dir):
|
|
accelerate_state_dict = accelerator.get_state_dict(models[-1], unwrap=True)
|
|
if accelerator.is_main_process:
|
|
from safetensors.torch import save_file
|
|
|
|
safetensor_save_path = os.path.join(output_dir, f"diffusion_pytorch_model.safetensors")
|
|
accelerate_state_dict = {k: v.to(dtype=weight_dtype) for k, v in accelerate_state_dict.items()}
|
|
save_file(accelerate_state_dict, safetensor_save_path, metadata={"format": "pt"})
|
|
|
|
with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file:
|
|
pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file)
|
|
|
|
def load_model_hook(models, input_dir):
|
|
pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl")
|
|
if os.path.exists(pkl_path):
|
|
with open(pkl_path, 'rb') as file:
|
|
loaded_number, _ = pickle.load(file)
|
|
batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0)
|
|
print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.")
|
|
else:
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
models[0].save_pretrained(os.path.join(output_dir, "transformer"))
|
|
if not args.use_deepspeed:
|
|
weights.pop()
|
|
|
|
with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file:
|
|
pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file)
|
|
|
|
def load_model_hook(models, input_dir):
|
|
for i in range(len(models)):
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
# load diffusers style into model
|
|
load_model = WanTransformer3DModel.from_pretrained(
|
|
input_dir, subfolder="transformer"
|
|
)
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl")
|
|
if os.path.exists(pkl_path):
|
|
with open(pkl_path, 'rb') as file:
|
|
loaded_number, _ = pickle.load(file)
|
|
batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0)
|
|
print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.")
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
if args.gradient_checkpointing:
|
|
transformer3d.enable_gradient_checkpointing()
|
|
|
|
# Enable TF32 for faster training on Ampere GPUs,
|
|
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
|
if args.allow_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Initialize the optimizer
|
|
if args.use_8bit_adam:
|
|
try:
|
|
import bitsandbytes as bnb
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
|
|
)
|
|
|
|
optimizer_cls = bnb.optim.AdamW8bit
|
|
elif args.use_came:
|
|
try:
|
|
from came_pytorch import CAME
|
|
except Exception:
|
|
raise ImportError(
|
|
"Please install came_pytorch to use CAME. You can do so by running `pip install came_pytorch`"
|
|
)
|
|
|
|
optimizer_cls = CAME
|
|
else:
|
|
optimizer_cls = torch.optim.AdamW
|
|
|
|
trainable_params = list(filter(lambda p: p.requires_grad, transformer3d.parameters()))
|
|
trainable_params_optim = [
|
|
{'params': [], 'lr': args.learning_rate},
|
|
{'params': [], 'lr': args.learning_rate / 2},
|
|
]
|
|
in_already = []
|
|
for name, param in transformer3d.named_parameters():
|
|
high_lr_flag = False
|
|
if name in in_already:
|
|
continue
|
|
for trainable_module_name in args.trainable_modules:
|
|
if trainable_module_name in name:
|
|
in_already.append(name)
|
|
high_lr_flag = True
|
|
trainable_params_optim[0]['params'].append(param)
|
|
if accelerator.is_main_process:
|
|
print(f"Set {name} to lr : {args.learning_rate}")
|
|
break
|
|
if high_lr_flag:
|
|
continue
|
|
for trainable_module_name in args.trainable_modules_low_learning_rate:
|
|
if trainable_module_name in name:
|
|
in_already.append(name)
|
|
trainable_params_optim[1]['params'].append(param)
|
|
if accelerator.is_main_process:
|
|
print(f"Set {name} to lr : {args.learning_rate / 2}")
|
|
break
|
|
|
|
if args.use_came:
|
|
optimizer = optimizer_cls(
|
|
trainable_params_optim,
|
|
lr=args.learning_rate,
|
|
# weight_decay=args.adam_weight_decay,
|
|
betas=(0.9, 0.999, 0.9999),
|
|
eps=(1e-30, 1e-16)
|
|
)
|
|
else:
|
|
optimizer = optimizer_cls(
|
|
trainable_params_optim,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Get the training dataset
|
|
sample_n_frames_bucket_interval = vae.config.temporal_compression_ratio
|
|
|
|
if args.fix_sample_size is not None and args.enable_bucket:
|
|
args.video_sample_size = max(max(args.fix_sample_size), args.video_sample_size)
|
|
args.image_sample_size = max(max(args.fix_sample_size), args.image_sample_size)
|
|
args.training_with_video_token_length = False
|
|
args.random_hw_adapt = False
|
|
|
|
# Get the dataset (Stage 1 always trains on raw videos with teacher forcing).
|
|
train_dataset = ImageVideoDataset(
|
|
args.train_data_meta, args.train_data_dir,
|
|
video_sample_size=args.video_sample_size, video_sample_stride=args.video_sample_stride, video_sample_n_frames=args.video_sample_n_frames,
|
|
video_repeat=args.video_repeat,
|
|
image_sample_size=args.image_sample_size,
|
|
enable_bucket=args.enable_bucket, enable_inpaint=False,
|
|
)
|
|
|
|
# Causal-Forcing needs at least one full block of latent frames per clip.
|
|
# Enforce the matching pixel-frame minimum at video-read time so too-short
|
|
# clips raise and get resampled instead of collapsing the block reshape.
|
|
if args.independent_first_frame:
|
|
# latent_frames - 1 must be >= num_frame_per_block
|
|
train_dataset.min_video_sample_n_frames = args.num_frame_per_block * sample_n_frames_bucket_interval + 1
|
|
else:
|
|
# latent_frames must be >= num_frame_per_block
|
|
train_dataset.min_video_sample_n_frames = (args.num_frame_per_block - 1) * sample_n_frames_bucket_interval + 1
|
|
|
|
def get_length_to_frame_num(token_length):
|
|
if args.image_sample_size > args.video_sample_size:
|
|
sample_sizes = list(range(args.video_sample_size, args.image_sample_size + 1, 128))
|
|
|
|
if sample_sizes[-1] != args.image_sample_size:
|
|
sample_sizes.append(args.image_sample_size)
|
|
else:
|
|
sample_sizes = [args.image_sample_size]
|
|
|
|
length_to_frame_num = {
|
|
sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1 for sample_size in sample_sizes
|
|
}
|
|
|
|
return length_to_frame_num
|
|
|
|
aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
batch_sampler_generator = torch.Generator().manual_seed(args.seed)
|
|
batch_sampler = AspectRatioBatchImageVideoSampler(
|
|
sampler=RandomSampler(train_dataset, generator=batch_sampler_generator), dataset=train_dataset.dataset,
|
|
batch_size=args.train_batch_size, train_folder = args.train_data_dir, drop_last=True,
|
|
aspect_ratios=aspect_ratio_sample_size,
|
|
)
|
|
|
|
def collate_fn(examples):
|
|
# Get token length
|
|
target_token_length = args.video_sample_n_frames * args.token_sample_size * args.token_sample_size
|
|
length_to_frame_num = get_length_to_frame_num(target_token_length)
|
|
|
|
# Create new output
|
|
new_examples = {}
|
|
new_examples["target_token_length"] = target_token_length
|
|
new_examples["pixel_values"] = []
|
|
new_examples["text"] = []
|
|
|
|
# Get downsample ratio in image and videos
|
|
pixel_value = examples[0]["pixel_values"]
|
|
data_type = examples[0]["data_type"]
|
|
f, h, w, c = np.shape(pixel_value)
|
|
if data_type == 'image':
|
|
random_downsample_ratio = 1 if not args.random_hw_adapt else get_random_downsample_ratio(args.image_sample_size, image_ratio=[args.image_sample_size / args.video_sample_size], rng=rng)
|
|
|
|
aspect_ratio_sample_size = {key : [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
|
|
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
else:
|
|
if args.random_hw_adapt:
|
|
if args.training_with_video_token_length:
|
|
local_min_size = np.min(np.array([np.mean(np.array([np.shape(example["pixel_values"])[1], np.shape(example["pixel_values"])[2]])) for example in examples]))
|
|
# The video will be resized to a lower resolution than its own.
|
|
choice_list = [length for length in list(length_to_frame_num.keys()) if length < local_min_size * 1.25]
|
|
if len(choice_list) == 0:
|
|
choice_list = list(length_to_frame_num.keys())
|
|
if rng is None:
|
|
local_video_sample_size = np.random.choice(choice_list)
|
|
else:
|
|
local_video_sample_size = rng.choice(choice_list)
|
|
batch_video_length = length_to_frame_num[local_video_sample_size]
|
|
random_downsample_ratio = args.video_sample_size / local_video_sample_size
|
|
else:
|
|
random_downsample_ratio = get_random_downsample_ratio(
|
|
args.video_sample_size, rng=rng)
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
else:
|
|
random_downsample_ratio = 1
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
|
|
aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
|
|
|
|
if args.fix_sample_size is not None:
|
|
fix_sample_size = [int(x / 16) * 16 for x in args.fix_sample_size]
|
|
elif args.random_ratio_crop:
|
|
if rng is None:
|
|
random_sample_size = aspect_ratio_random_crop_sample_size[
|
|
np.random.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB)
|
|
]
|
|
else:
|
|
random_sample_size = aspect_ratio_random_crop_sample_size[
|
|
rng.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB)
|
|
]
|
|
random_sample_size = [int(x / 16) * 16 for x in random_sample_size]
|
|
else:
|
|
closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size)
|
|
closest_size = [int(x / 16) * 16 for x in closest_size]
|
|
|
|
min_example_length = min(
|
|
[example["pixel_values"].shape[0] for example in examples]
|
|
)
|
|
batch_video_length = int(min(batch_video_length, min_example_length))
|
|
|
|
# Magvae needs the number of frames to be 4n + 1.
|
|
batch_video_length = (batch_video_length - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1
|
|
|
|
# Causal-Forcing needs the latent frame count to align with num_frame_per_block.
|
|
# Always keep at least one full causal block so the per-block timestep
|
|
# reshape (num_frames -> [-1, num_frame_per_block]) stays valid.
|
|
k = (batch_video_length - 1) // sample_n_frames_bucket_interval
|
|
if args.independent_first_frame:
|
|
# latent_frames - 1 = k must be a positive multiple of num_frame_per_block
|
|
k = max(k // args.num_frame_per_block, 1) * args.num_frame_per_block
|
|
else:
|
|
# latent_frames = k + 1 must be a positive multiple of num_frame_per_block
|
|
k = max((k + 1) // args.num_frame_per_block, 1) * args.num_frame_per_block - 1
|
|
batch_video_length = k * sample_n_frames_bucket_interval + 1
|
|
|
|
for example in examples:
|
|
if args.fix_sample_size is not None:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
fix_sample_size = list(map(lambda x: int(x), fix_sample_size))
|
|
transform = transforms.Compose([
|
|
transforms.Resize(fix_sample_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(fix_sample_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
elif args.random_ratio_crop:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
b, c, h, w = pixel_values.size()
|
|
th, tw = random_sample_size
|
|
if th / tw > h / w:
|
|
nh = int(th)
|
|
nw = int(w / h * nh)
|
|
else:
|
|
nw = int(tw)
|
|
nh = int(h / w * nw)
|
|
|
|
transform = transforms.Compose([
|
|
transforms.Resize([nh, nw]),
|
|
transforms.CenterCrop([int(x) for x in random_sample_size]),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
else:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
closest_size = list(map(lambda x: int(x), closest_size))
|
|
if closest_size[0] / h > closest_size[1] / w:
|
|
resize_size = closest_size[0], int(w * closest_size[0] / h)
|
|
else:
|
|
resize_size = int(h * closest_size[1] / w), closest_size[1]
|
|
|
|
transform = transforms.Compose([
|
|
transforms.Resize(resize_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(closest_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
|
|
new_examples["pixel_values"].append(transform(pixel_values)[:batch_video_length])
|
|
new_examples["text"].append(example["text"])
|
|
|
|
# Limit the number of frames to the same
|
|
new_examples["pixel_values"] = torch.stack([example for example in new_examples["pixel_values"]])
|
|
|
|
# Encode prompts when enable_text_encoder_in_dataloader=True
|
|
if args.enable_text_encoder_in_dataloader:
|
|
prompt_ids = tokenizer(
|
|
new_examples['text'],
|
|
max_length=args.tokenizer_max_length,
|
|
padding="max_length",
|
|
add_special_tokens=True,
|
|
truncation=True,
|
|
return_tensors="pt"
|
|
)
|
|
text_input_ids = prompt_ids.input_ids
|
|
prompt_attention_mask = prompt_ids.attention_mask
|
|
|
|
seq_lens = prompt_attention_mask.gt(0).sum(dim=1).long()
|
|
prompt_embeds = text_encoder(text_input_ids.to("cpu"), attention_mask=prompt_attention_mask.to("cpu"))[0]
|
|
prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
|
|
|
|
new_examples['encoder_attention_mask'] = prompt_ids.attention_mask
|
|
new_examples['encoder_hidden_states'] = prompt_embeds
|
|
|
|
return new_examples
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
batch_sampler=batch_sampler,
|
|
collate_fn=collate_fn,
|
|
persistent_workers=True if args.dataloader_num_workers != 0 else False,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
|
|
num_training_steps=args.max_train_steps * accelerator.num_processes,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
if fsdp_stage != 0 or zero_stage != 0:
|
|
from functools import partial
|
|
|
|
from videox_fun.dist import set_multi_gpus_devices, shard_model
|
|
shard_fn = partial(shard_model, device_id=accelerator.device, param_dtype=weight_dtype)
|
|
text_encoder = shard_fn(text_encoder)
|
|
|
|
# Move text_encode and vae to gpu and cast to weight_dtype
|
|
vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_encoder.to(accelerator.device if not args.low_vram else "cpu")
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if accelerator.is_main_process:
|
|
tracker_config = dict(vars(args))
|
|
keys_to_pop = [k for k, v in tracker_config.items() if isinstance(v, list)]
|
|
for k in keys_to_pop:
|
|
tracker_config.pop(k)
|
|
print(f"Removed tracker_config['{k}']")
|
|
accelerator.init_trackers(args.tracker_project_name, tracker_config)
|
|
|
|
# Function for unwrapping if model was compiled with `torch.compile`.
|
|
def unwrap_model(model):
|
|
model = accelerator.unwrap_model(model)
|
|
model = model._orig_mod if is_compiled_module(model) else model
|
|
return model
|
|
|
|
# Train!
|
|
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
global_step = 0
|
|
first_epoch = 0
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_checkpoint:
|
|
if args.resume_from_checkpoint != "latest":
|
|
path = os.path.basename(args.resume_from_checkpoint)
|
|
else:
|
|
# Get the most recent checkpoint
|
|
dirs = os.listdir(args.output_dir)
|
|
dirs = [d for d in dirs if d.startswith("checkpoint")]
|
|
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
|
|
path = dirs[-1] if len(dirs) > 0 else None
|
|
|
|
if path is None:
|
|
accelerator.print(
|
|
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
|
|
)
|
|
args.resume_from_checkpoint = None
|
|
initial_global_step = 0
|
|
else:
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
|
|
pkl_path = os.path.join(os.path.join(args.output_dir, path), "sampler_pos_start.pkl")
|
|
if os.path.exists(pkl_path):
|
|
with open(pkl_path, 'rb') as file:
|
|
_, first_epoch = pickle.load(file)
|
|
else:
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
print(f"Load pkl from {pkl_path}. Get first_epoch = {first_epoch}.")
|
|
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
else:
|
|
initial_global_step = 0
|
|
|
|
progress_bar = tqdm(
|
|
range(0, args.max_train_steps),
|
|
initial=initial_global_step,
|
|
desc="Steps",
|
|
# Only show the progress bar once on each machine.
|
|
disable=not accelerator.is_local_main_process,
|
|
)
|
|
|
|
# Materialise the full per-step schedule once so that `add_noise` and the
|
|
# training-weight table can be looked up by timestep.
|
|
noise_scheduler.set_timesteps(args.train_sampling_steps, device=accelerator.device)
|
|
schedule_timesteps = noise_scheduler.timesteps.clone()
|
|
# Causal-Forcing per-timestep loss weight (Gaussian centered at T/2). Mirrors
|
|
# `FlowMatchScheduler.set_timesteps(training=True)` in Causal-Forcing.
|
|
_x = schedule_timesteps.float()
|
|
_y = torch.exp(-2 * ((_x - args.train_sampling_steps / 2) / args.train_sampling_steps) ** 2)
|
|
_y_shifted = _y - _y.min()
|
|
training_weights_table = _y_shifted * (args.train_sampling_steps / _y_shifted.sum().clamp_min(1e-12))
|
|
training_weights_table = training_weights_table.to(accelerator.device)
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
train_loss = 0.0
|
|
batch_sampler.sampler.generator = torch.Generator().manual_seed(args.seed + epoch)
|
|
for step, batch in enumerate(train_dataloader):
|
|
# Data batch sanity check (only available when training on raw videos)
|
|
if epoch == first_epoch and step == 0:
|
|
pixel_values, texts = batch['pixel_values'].cpu(), batch['text']
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True)
|
|
for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}.mp4", rescale=True)
|
|
|
|
with torch.amp.autocast('cuda', dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
|
|
# Convert prompts to text embeddings.
|
|
if args.enable_text_encoder_in_dataloader:
|
|
prompt_embeds = batch['encoder_hidden_states'].to(device=accelerator.device)
|
|
else:
|
|
with torch.no_grad():
|
|
prompt_ids = tokenizer(
|
|
batch['text'],
|
|
padding="max_length",
|
|
max_length=args.tokenizer_max_length,
|
|
truncation=True,
|
|
add_special_tokens=True,
|
|
return_tensors="pt"
|
|
)
|
|
text_input_ids = prompt_ids.input_ids
|
|
prompt_attention_mask = prompt_ids.attention_mask
|
|
|
|
seq_lens = prompt_attention_mask.gt(0).sum(dim=1).long()
|
|
# Under --low_vram the encoder is parked on CPU; move it to
|
|
# the device just for this forward pass and park it back.
|
|
if args.low_vram:
|
|
text_encoder.to(accelerator.device)
|
|
prompt_embeds = text_encoder(text_input_ids.to(accelerator.device), attention_mask=prompt_attention_mask.to(accelerator.device))[0]
|
|
prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
|
|
if args.low_vram:
|
|
text_encoder.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
|
|
# Convert pixel videos to clean latents via the VAE.
|
|
pixel_values = batch["pixel_values"].to(weight_dtype)
|
|
if args.low_vram:
|
|
torch.cuda.empty_cache()
|
|
vae.to(accelerator.device)
|
|
|
|
with torch.no_grad():
|
|
def _batch_encode_vae(pixel_values):
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
bs = args.vae_mini_batch
|
|
new_pixel_values = []
|
|
for i in range(0, pixel_values.shape[0], bs):
|
|
pixel_values_bs = pixel_values[i : i + bs]
|
|
pixel_values_bs = vae.encode(pixel_values_bs)[0]
|
|
pixel_values_bs = pixel_values_bs.sample()
|
|
new_pixel_values.append(pixel_values_bs)
|
|
return torch.cat(new_pixel_values, dim=0)
|
|
clean_latents = _batch_encode_vae(pixel_values)
|
|
|
|
if args.low_vram:
|
|
vae.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
|
|
with accelerator.accumulate(transformer3d):
|
|
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
|
|
sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
|
|
schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
|
|
timesteps = timesteps.to(accelerator.device)
|
|
step_indices = torch.argmin((schedule_timesteps.unsqueeze(0) - timesteps.reshape(-1).unsqueeze(1)).abs(), dim=1)
|
|
sigma = sigmas[step_indices]
|
|
while len(sigma.shape) < n_dim:
|
|
sigma = sigma.unsqueeze(-1)
|
|
return sigma
|
|
|
|
def add_noise(latents, noise, timesteps):
|
|
"""Per-frame flow-matching add_noise; supports timesteps of shape [B, F]."""
|
|
sigmas = get_sigmas(timesteps, n_dim=2, dtype=latents.dtype)
|
|
sigmas = sigmas.reshape(latents.shape[0], latents.shape[2], 1, 1, 1).permute(0, 2, 1, 3, 4)
|
|
return (1.0 - sigmas) * latents + sigmas * noise
|
|
|
|
def get_per_block_timestep(min_step, max_step, batch_size, num_frames):
|
|
"""One timestep per causal block; frames inside the same block share it."""
|
|
timestep = torch.randint(
|
|
min_step, max_step, [batch_size, num_frames],
|
|
device=accelerator.device, dtype=torch.long, generator=torch_rng,
|
|
)
|
|
if args.independent_first_frame:
|
|
ts_rest = timestep[:, 1:]
|
|
ts_rest = ts_rest.reshape(ts_rest.shape[0], -1, args.num_frame_per_block)
|
|
ts_rest[:, :, 1:] = ts_rest[:, :, 0:1]
|
|
ts_rest = ts_rest.reshape(ts_rest.shape[0], -1)
|
|
timestep = torch.cat([timestep[:, 0:1], ts_rest], dim=1)
|
|
else:
|
|
timestep = timestep.reshape(timestep.shape[0], -1, args.num_frame_per_block)
|
|
timestep[:, :, 1:] = timestep[:, :, 0:1]
|
|
timestep = timestep.reshape(timestep.shape[0], -1)
|
|
return timestep
|
|
|
|
bsz, channel, num_frames, height, width = clean_latents.shape
|
|
|
|
# Sample per-block timesteps and build the noisy input.
|
|
# Mirrors `BaseModel._get_timestep` in Causal-Forcing.
|
|
step_index = get_per_block_timestep(
|
|
int(0.02 * args.train_sampling_steps),
|
|
int(0.98 * args.train_sampling_steps),
|
|
bsz, num_frames,
|
|
)
|
|
timestep = schedule_timesteps.to(accelerator.device)[step_index].to(dtype=torch.float32)
|
|
|
|
noise = torch.randn(clean_latents.shape, dtype=weight_dtype, device=accelerator.device, generator=torch_rng)
|
|
noisy_latents = add_noise(clean_latents, noise, timestep)
|
|
training_target = noise - clean_latents # flow-matching target
|
|
|
|
# Optional noise augmentation on the clean context tokens.
|
|
if (not args.no_teacher_forcing) and args.noise_augmentation_max_timestep > 0:
|
|
aug_index = get_per_block_timestep(
|
|
0, args.noise_augmentation_max_timestep, bsz, num_frames,
|
|
)
|
|
aug_t = schedule_timesteps.to(accelerator.device)[aug_index].to(dtype=torch.float32)
|
|
clean_latents_aug = add_noise(clean_latents, noise, aug_t)
|
|
else:
|
|
clean_latents_aug = clean_latents
|
|
aug_t = None
|
|
|
|
with torch.amp.autocast('cuda', dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
|
|
# The transformer expects List[Tensor] of shape [C, F, H, W] per item.
|
|
noisy_input_list = [noisy_latents[i] for i in range(bsz)]
|
|
clean_x_list = [clean_latents_aug[i] for i in range(bsz)] if not args.no_teacher_forcing else None
|
|
patch_h, patch_w = accelerator.unwrap_model(transformer3d).config.patch_size[1:]
|
|
full_seq_len = num_frames * height * width // (patch_h * patch_w)
|
|
|
|
flow_pred = transformer3d(
|
|
x=noisy_input_list,
|
|
context=prompt_embeds,
|
|
t=timestep.to(torch.int64),
|
|
seq_len=full_seq_len,
|
|
clean_x=clean_x_list,
|
|
aug_t=(aug_t.to(torch.int64) if aug_t is not None else None),
|
|
)
|
|
if isinstance(flow_pred, list):
|
|
flow_pred = torch.stack(flow_pred, dim=0)
|
|
|
|
# Per-frame flow-matching MSE; optionally weighted by the Causal-Forcing
|
|
# Gaussian timestep weight.
|
|
loss_per_frame = F.mse_loss(
|
|
flow_pred.float(), training_target.float(), reduction='none'
|
|
).mean(dim=(1, 3, 4)) # [B, F]
|
|
if args.use_timestep_weight:
|
|
flat_t = timestep.reshape(-1).to(training_weights_table.dtype)
|
|
schedule_for_lookup = schedule_timesteps.to(accelerator.device, dtype=training_weights_table.dtype)
|
|
weight_idx = torch.argmin((schedule_for_lookup.unsqueeze(0) - flat_t.unsqueeze(1)).abs(), dim=1)
|
|
weights = training_weights_table[weight_idx].reshape(timestep.shape)
|
|
loss = (loss_per_frame * weights.to(loss_per_frame.dtype)).mean()
|
|
else:
|
|
loss = loss_per_frame.mean()
|
|
|
|
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
|
|
train_loss += avg_loss.item() / args.gradient_accumulation_steps
|
|
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
accelerator.log({"train_loss": train_loss}, step=global_step)
|
|
train_loss = 0.0
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
if args.use_deepspeed or args.use_fsdp or accelerator.is_main_process:
|
|
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
|
|
if args.checkpoints_total_limit is not None:
|
|
checkpoints = os.listdir(args.output_dir)
|
|
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
|
|
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
|
|
|
|
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
|
|
if len(checkpoints) >= args.checkpoints_total_limit:
|
|
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
|
|
removing_checkpoints = checkpoints[0:num_to_remove]
|
|
|
|
logger.info(
|
|
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
|
|
)
|
|
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
|
|
|
|
for removing_checkpoint in removing_checkpoints:
|
|
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
|
|
shutil.rmtree(removing_checkpoint)
|
|
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(save_path)
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
transformer3d,
|
|
args,
|
|
config,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
transformer3d,
|
|
args,
|
|
config,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
# Create the pipeline using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
transformer3d = unwrap_model(transformer3d)
|
|
|
|
if args.use_deepspeed or args.use_fsdp or accelerator.is_main_process:
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(save_path)
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|