914 lines
47 KiB
Python
914 lines
47 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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A minimal training script for DiT using PyTorch DDP.
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"""
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import argparse
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import logging
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import math
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import os
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import shutil
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from pathlib import Path
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import gc
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import numpy as np
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from einops import rearrange
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from tqdm import tqdm
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from opensora.adaptor.modules import replace_with_fp32_forwards
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from opensora.utils.parallel_states import initialize_sequence_parallel_state, \
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destroy_sequence_parallel_group, get_sequence_parallel_state, set_sequence_parallel_state
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from opensora.utils.communications import prepare_parallel_data, broadcast
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import time
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from torch.utils.data import DataLoader
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from copy import deepcopy
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import accelerate
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import torch
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from torch.nn import functional as F
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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.utils import DistributedType, ProjectConfiguration, set_seed
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from packaging import version
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from tqdm.auto import tqdm
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import diffusers
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from diffusers import DDPMScheduler, PNDMScheduler, DPMSolverMultistepScheduler
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import compute_snr
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from diffusers.utils import check_min_version, is_wandb_available
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from opensora.utils.ema import EMAModel
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from opensora.dataset import getdataset
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from opensora.models import CausalVAEModelWrapper
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from opensora.models.text_encoder import get_text_enc, get_text_warpper
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from opensora.models.causalvideovae import ae_stride_config, ae_channel_config
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from opensora.models.causalvideovae import ae_norm, ae_denorm
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from opensora.models.diffusion import Diffusion_models, Diffusion_models_class
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from opensora.utils.dataset_utils import Collate, LengthGroupedSampler
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from opensora.sample.pipeline_opensora import OpenSoraPipeline
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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("0.24.0")
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logger = get_logger(__name__)
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@torch.inference_mode()
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def log_validation(args, model, vae, text_encoder, tokenizer, accelerator, weight_dtype, global_step, ema=False):
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positive_prompt = "(masterpiece), (best quality), (ultra-detailed), {}. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous"
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negative_prompt = """nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,
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"""
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validation_prompt = [
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"A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.",
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"A serene underwater scene featuring a sea turtle swimming through a coral reef. The turtle, with its greenish-brown shell, is the main focus of the video, swimming gracefully towards the right side of the frame. The coral reef, teeming with life, is visible in the background, providing a vibrant and colorful backdrop to the turtle's journey. Several small fish, darting around the turtle, add a sense of movement and dynamism to the scene."
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]
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if 'mt5' in args.text_encoder_name:
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validation_prompt_cn = [
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"一只戴着墨镜在泳池当救生员的猫咪。",
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"这是一个宁静的水下场景，一只海龟游过珊瑚礁。海龟带着绿褐色的龟壳，优雅地游向画面右侧，成为视频的焦点。背景中的珊瑚礁生机盎然，为海龟的旅程提供了生动多彩的背景。几条小鱼在海龟周围穿梭，为画面增添了动感和活力。"
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]
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validation_prompt += validation_prompt_cn
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logger.info(f"Running validation....\n")
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model = accelerator.unwrap_model(model)
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scheduler = DPMSolverMultistepScheduler()
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opensora_pipeline = OpenSoraPipeline(vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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scheduler=scheduler,
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transformer=model).to(device=accelerator.device)
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videos = []
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for prompt in validation_prompt:
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logger.info('Processing the ({}) prompt'.format(prompt))
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video = opensora_pipeline(
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positive_prompt.format(prompt),
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negative_prompt=negative_prompt,
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num_frames=args.num_frames,
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height=args.max_height,
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width=args.max_width,
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num_inference_steps=args.num_sampling_steps,
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guidance_scale=args.guidance_scale,
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enable_temporal_attentions=True,
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num_images_per_prompt=1,
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mask_feature=True,
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max_sequence_length=args.model_max_length,
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).images
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videos.append(video[0])
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# import ipdb;ipdb.set_trace()
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gc.collect()
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torch.cuda.empty_cache()
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videos = torch.stack(videos).numpy()
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videos = rearrange(videos, 'b t h w c -> b t c h w')
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for tracker in accelerator.trackers:
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if tracker.name == "tensorboard":
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if videos.shape[1] == 1:
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assert args.num_frames == 1
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images = rearrange(videos, 'b 1 c h w -> (b 1) h w c')
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np_images = np.stack([np.asarray(img) for img in images])
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tracker.writer.add_images(f"{'ema_' if ema else ''}validation", np_images, global_step, dataformats="NHWC")
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else:
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np_videos = np.stack([np.asarray(vid) for vid in videos])
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tracker.writer.add_video(f"{'ema_' if ema else ''}validation", np_videos, global_step, fps=24)
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if tracker.name == "wandb":
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import wandb
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if videos.shape[1] == 1:
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images = rearrange(videos, 'b 1 c h w -> (b 1) h w c')
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logs = {
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f"{'ema_' if ema else ''}validation": [
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wandb.Image(image, caption=f"{i}: {prompt}")
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for i, (image, prompt) in enumerate(zip(images, validation_prompt))
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]
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}
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else:
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logs = {
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f"{'ema_' if ema else ''}validation": [
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wandb.Video(video, caption=f"{i}: {prompt}", fps=24)
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for i, (video, prompt) in enumerate(zip(videos, validation_prompt))
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]
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}
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tracker.log(logs, step=global_step)
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del opensora_pipeline
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gc.collect()
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torch.cuda.empty_cache()
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class ProgressInfo:
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def __init__(self, global_step, train_loss=0.0):
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self.global_step = global_step
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self.train_loss = train_loss
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#################################################################################
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# Training Loop #
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#################################################################################
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def main(args):
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logging_dir = Path(args.output_dir, args.logging_dir)
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# use LayerNorm, GeLu, SiLu always as fp32 mode
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if args.enable_stable_fp32:
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replace_with_fp32_forwards()
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accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with=args.report_to,
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project_config=accelerator_project_config,
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)
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if args.num_frames != 1 and args.use_image_num == 0:
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initialize_sequence_parallel_state(args.sp_size)
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if args.report_to == "wandb":
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if not is_wandb_available():
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raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state, main_process_only=False)
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if accelerator.is_local_main_process:
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transformers.utils.logging.set_verbosity_warning()
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diffusers.utils.logging.set_verbosity_info()
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else:
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transformers.utils.logging.set_verbosity_error()
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diffusers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now. On GPU...
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if args.seed is not None:
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set_seed(args.seed)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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# For mixed precision training we cast all non-trainable weigths to half-precision
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# as these weights are only used for inference, keeping weights in full precision is not required.
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weight_dtype = torch.float32
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if accelerator.mixed_precision == "fp16":
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weight_dtype = torch.float16
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elif accelerator.mixed_precision == "bf16":
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weight_dtype = torch.bfloat16
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# Create model:
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kwargs = {}
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ae = CausalVAEModelWrapper(args.ae_path, cache_dir=args.cache_dir, **kwargs).eval()
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if args.enable_tiling:
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ae.vae.enable_tiling()
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ae.vae.tile_overlap_factor = args.tile_overlap_factor
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kwargs = {'load_in_8bit': args.enable_8bit_t5, 'torch_dtype': weight_dtype, 'low_cpu_mem_usage': True}
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text_enc = get_text_warpper(args.text_encoder_name)(args, **kwargs).eval()
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ae_stride_t, ae_stride_h, ae_stride_w = ae_stride_config[args.ae]
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ae.vae_scale_factor = (ae_stride_t, ae_stride_h, ae_stride_w)
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assert ae_stride_h == ae_stride_w, f"Support only ae_stride_h == ae_stride_w now, but found ae_stride_h ({ae_stride_h}), ae_stride_w ({ae_stride_w})"
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args.ae_stride_t, args.ae_stride_h, args.ae_stride_w = ae_stride_t, ae_stride_h, ae_stride_w
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args.ae_stride = args.ae_stride_h
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patch_size = args.model[-3:]
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patch_size_t, patch_size_h, patch_size_w = int(patch_size[0]), int(patch_size[1]), int(patch_size[2])
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args.patch_size = patch_size_h
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args.patch_size_t, args.patch_size_h, args.patch_size_w = patch_size_t, patch_size_h, patch_size_w
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assert patch_size_h == patch_size_w, f"Support only patch_size_h == patch_size_w now, but found patch_size_h ({patch_size_h}), patch_size_w ({patch_size_w})"
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# assert args.num_frames % ae_stride_t == 0, f"Num_frames must be divisible by ae_stride_t, but found num_frames ({args.num_frames}), ae_stride_t ({ae_stride_t})."
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assert args.max_height % ae_stride_h == 0, f"Height must be divisible by ae_stride_h, but found Height ({args.max_height}), ae_stride_h ({ae_stride_h})."
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assert args.max_width % ae_stride_h == 0, f"Width size must be divisible by ae_stride_h, but found Width ({args.max_width}), ae_stride_h ({ae_stride_h})."
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args.stride_t = ae_stride_t * patch_size_t
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args.stride = ae_stride_h * patch_size_h
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latent_size = (args.max_height // ae_stride_h, args.max_width // ae_stride_w)
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ae.latent_size = latent_size
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if args.num_frames % 2 == 1:
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args.latent_size_t = latent_size_t = (args.num_frames - 1) // ae_stride_t + 1
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else:
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latent_size_t = args.num_frames // ae_stride_t
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model = Diffusion_models[args.model](
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in_channels=ae_channel_config[args.ae],
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out_channels=ae_channel_config[args.ae],
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# caption_channels=4096,
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# cross_attention_dim=1152,
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attention_bias=True,
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sample_size=latent_size,
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sample_size_t=latent_size_t,
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num_vector_embeds=None,
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activation_fn="gelu-approximate",
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num_embeds_ada_norm=1000,
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use_linear_projection=False,
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only_cross_attention=False,
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double_self_attention=False,
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upcast_attention=False,
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# norm_type="ada_norm_single",
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norm_elementwise_affine=False,
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norm_eps=1e-6,
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attention_type='default',
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attention_mode=args.attention_mode,
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interpolation_scale_h=args.interpolation_scale_h,
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interpolation_scale_w=args.interpolation_scale_w,
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interpolation_scale_t=args.interpolation_scale_t,
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downsampler=args.downsampler,
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use_rope=args.use_rope,
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# model_max_length=args.model_max_length,
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use_stable_fp32=args.enable_stable_fp32,
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)
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model.gradient_checkpointing = args.gradient_checkpointing
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# # use pretrained model?
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if args.pretrained:
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model_state_dict = model.state_dict()
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if 'safetensors' in args.pretrained: # pixart series
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from safetensors.torch import load_file as safe_load
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# import ipdb;ipdb.set_trace()
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pretrained_checkpoint = safe_load(args.pretrained, device="cpu")
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pretrained_keys = set(list(pretrained_checkpoint.keys()))
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model_keys = set(list(model_state_dict.keys()))
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common_keys = list(pretrained_keys & model_keys)
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checkpoint = {k: pretrained_checkpoint[k] for k in common_keys if model_state_dict[k].numel() == pretrained_checkpoint[k].numel()}
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# if checkpoint['pos_embed.proj.weight'].shape != model.pos_embed.proj.weight.shape and checkpoint['pos_embed.proj.weight'].ndim == 4:
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# logger.info(f"Resize pos_embed, {checkpoint['pos_embed.proj.weight'].shape} -> {model.pos_embed.proj.weight.shape}")
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# repeat = model.pos_embed.proj.weight.shape[2]
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# checkpoint['pos_embed.proj.weight'] = checkpoint['pos_embed.proj.weight'].unsqueeze(2).repeat(1, 1, repeat, 1, 1) / float(repeat)
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# del checkpoint['proj_out.weight'], checkpoint['proj_out.bias']
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else: # latest stage training weight
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checkpoint = torch.load(args.pretrained, map_location='cpu')
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if 'model' in checkpoint:
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checkpoint = checkpoint['model']
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missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)
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logger.info(f'missing_keys {len(missing_keys)} {missing_keys}, unexpected_keys {len(unexpected_keys)}')
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logger.info(f'Successfully load {len(model_state_dict) - len(missing_keys)}/{len(model_state_dict)} keys from {args.pretrained}!')
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# Freeze vae and text encoders.
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ae.vae.requires_grad_(False)
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text_enc.requires_grad_(False)
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# Set model as trainable.
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model.train()
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noise_scheduler = DDPMScheduler()
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# Move unet, vae and text_encoder to device and cast to weight_dtype
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# The VAE is in float32 to avoid NaN losses.
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ae.vae.to(accelerator.device, dtype=torch.float32)
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# ae.vae.to(accelerator.device, dtype=weight_dtype)
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text_enc.to(accelerator.device, dtype=weight_dtype)
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# Create EMA for the unet.
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if args.use_ema:
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ema_model = deepcopy(model)
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ema_model = EMAModel(ema_model.parameters(), decay=args.ema_decay, update_after_step=args.ema_start_step,
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model_cls=Diffusion_models_class[args.model], model_config=ema_model.config)
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# `accelerate` 0.16.0 will have better support for customized saving
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if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
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# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
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def save_model_hook(models, weights, output_dir):
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if accelerator.is_main_process:
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if args.use_ema:
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ema_model.save_pretrained(os.path.join(output_dir, "model_ema"))
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for i, model in enumerate(models):
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model.save_pretrained(os.path.join(output_dir, "model"))
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if weights: # Don't pop if empty
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# make sure to pop weight so that corresponding model is not saved again
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weights.pop()
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def load_model_hook(models, input_dir):
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if args.use_ema:
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load_model = EMAModel.from_pretrained(os.path.join(input_dir, "model_ema"), Diffusion_models_class[args.model])
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ema_model.load_state_dict(load_model.state_dict())
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ema_model.to(accelerator.device)
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del load_model
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for i in range(len(models)):
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# pop models so that they are not loaded again
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model = models.pop()
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# load diffusers style into model
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load_model = Diffusion_models_class[args.model].from_pretrained(input_dir, subfolder="model")
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model.register_to_config(**load_model.config)
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model.load_state_dict(load_model.state_dict())
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del load_model
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accelerator.register_save_state_pre_hook(save_model_hook)
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accelerator.register_load_state_pre_hook(load_model_hook)
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# Enable TF32 for faster training on Ampere GPUs,
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# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
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if args.allow_tf32:
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torch.backends.cuda.matmul.allow_tf32 = True
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if args.scale_lr:
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args.learning_rate = (
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args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
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)
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params_to_optimize = model.parameters()
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# Optimizer creation
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if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
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logger.warning(
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f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
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"Defaulting to adamW"
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)
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args.optimizer = "adamw"
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if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
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logger.warning(
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f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
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f"set to {args.optimizer.lower()}"
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)
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if args.optimizer.lower() == "adamw":
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if args.use_8bit_adam:
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try:
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import bitsandbytes as bnb
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except ImportError:
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raise ImportError(
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"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
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)
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optimizer_class = bnb.optim.AdamW8bit
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else:
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optimizer_class = torch.optim.AdamW
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optimizer = optimizer_class(
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params_to_optimize,
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lr=args.learning_rate,
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betas=(args.adam_beta1, args.adam_beta2),
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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if args.optimizer.lower() == "prodigy":
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try:
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import prodigyopt
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except ImportError:
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raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
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optimizer_class = prodigyopt.Prodigy
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|
|
if args.learning_rate <= 0.1:
|
|
logger.warning(
|
|
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
|
|
)
|
|
|
|
optimizer = optimizer_class(
|
|
params_to_optimize,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
beta3=args.prodigy_beta3,
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
decouple=args.prodigy_decouple,
|
|
use_bias_correction=args.prodigy_use_bias_correction,
|
|
safeguard_warmup=args.prodigy_safeguard_warmup,
|
|
)
|
|
logger.info(f"optimizer: {optimizer}")
|
|
|
|
# Setup data:
|
|
train_dataset = getdataset(args)
|
|
sampler = LengthGroupedSampler(
|
|
args.train_batch_size,
|
|
world_size=accelerator.num_processes,
|
|
lengths=train_dataset.lengths,
|
|
group_frame=args.group_frame,
|
|
group_resolution=args.group_resolution,
|
|
) if (args.group_frame or args.group_resolution) else None
|
|
train_dataloader = DataLoader(
|
|
train_dataset,
|
|
shuffle=sampler is None,
|
|
# pin_memory=True,
|
|
collate_fn=Collate(args),
|
|
batch_size=args.train_batch_size,
|
|
num_workers=args.dataloader_num_workers,
|
|
sampler=sampler if (args.group_frame or args.group_resolution) else None,
|
|
drop_last=True,
|
|
# prefetch_factor=4
|
|
)
|
|
logger.info(f'after train_dataloader')
|
|
|
|
# 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 * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
# model.requires_grad_(False)
|
|
# model.pos_embed.requires_grad_(True)
|
|
logger.info(f'before accelerator.prepare')
|
|
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
model, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
logger.info(f'after accelerator.prepare')
|
|
if args.use_ema:
|
|
ema_model.to(accelerator.device)
|
|
|
|
# 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:
|
|
accelerator.init_trackers(os.path.basename(args.output_dir), config=vars(args))
|
|
|
|
# Train!
|
|
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
total_batch_size = total_batch_size // args.sp_size * args.train_sp_batch_size
|
|
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}")
|
|
logger.info(f" Total training parameters = {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e9} B")
|
|
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:
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
|
|
|
|
|
|
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,
|
|
)
|
|
progress_info = ProgressInfo(global_step, train_loss=0.0)
|
|
|
|
def sync_gradients_info(loss):
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if args.use_ema:
|
|
ema_model.step(model.parameters())
|
|
progress_bar.update(1)
|
|
progress_info.global_step += 1
|
|
end_time = time.time()
|
|
one_step_duration = end_time - start_time
|
|
accelerator.log({"train_loss": progress_info.train_loss}, step=progress_info.global_step)
|
|
progress_info.train_loss = 0.0
|
|
|
|
# DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
|
|
if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
|
|
if progress_info.global_step % args.checkpointing_steps == 0:
|
|
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
|
|
if accelerator.is_main_process and 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)
|
|
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{progress_info.global_step}")
|
|
accelerator.save_state(save_path)
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
def run(model_input, model_kwargs, prof):
|
|
global start_time
|
|
start_time = time.time()
|
|
|
|
noise = torch.randn_like(model_input)
|
|
if args.noise_offset:
|
|
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
|
noise += args.noise_offset * torch.randn((model_input.shape[0], model_input.shape[1], 1, 1, 1),
|
|
device=model_input.device)
|
|
|
|
bsz = model_input.shape[0]
|
|
current_step_frame = model_input.shape[2]
|
|
# Sample a random timestep for each image without bias.
|
|
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device)
|
|
if current_step_frame != 1 and get_sequence_parallel_state(): # image do not need sp
|
|
broadcast(timesteps)
|
|
|
|
# Add noise to the model input according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
|
|
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
|
|
|
|
model_pred = model(
|
|
noisy_model_input,
|
|
timesteps,
|
|
**model_kwargs
|
|
)[0]
|
|
# Get the target for loss depending on the prediction type
|
|
if args.prediction_type is not None:
|
|
# set prediction_type of scheduler if defined
|
|
noise_scheduler.register_to_config(prediction_type=args.prediction_type)
|
|
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
|
|
elif noise_scheduler.config.prediction_type == "sample":
|
|
# We set the target to latents here, but the model_pred will return the noise sample prediction.
|
|
target = model_input
|
|
# We will have to subtract the noise residual from the prediction to get the target sample.
|
|
model_pred = model_pred - noise
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
mask = model_kwargs.get('attention_mask', None)
|
|
if torch.all(mask.bool()):
|
|
mask = None
|
|
if get_sequence_parallel_state():
|
|
assert mask is None
|
|
b, c, _, _, _ = model_pred.shape
|
|
if mask is not None:
|
|
mask = mask.unsqueeze(1).repeat(1, c, 1, 1, 1).float() # b t h w -> b c t h w
|
|
mask = mask.reshape(b, -1)
|
|
if args.snr_gamma is None:
|
|
# model_pred: b c t h w, attention_mask: b t h w
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
|
|
loss = loss.reshape(b, -1)
|
|
if mask is not None:
|
|
loss = (loss * mask).sum() / mask.sum() # mean loss on unpad patches
|
|
else:
|
|
loss = loss.mean()
|
|
else:
|
|
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
|
|
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
|
|
# This is discussed in Section 4.2 of the same paper.
|
|
snr = compute_snr(noise_scheduler, timesteps)
|
|
mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(
|
|
dim=1
|
|
)[0]
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
mse_loss_weights = mse_loss_weights / snr
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
mse_loss_weights = mse_loss_weights / (snr + 1)
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
|
|
loss = loss.reshape(b, -1)
|
|
mse_loss_weights = mse_loss_weights.reshape(b, 1)
|
|
if mask is not None:
|
|
loss = (loss * mask * mse_loss_weights).sum() / mask.sum() # mean loss on unpad patches
|
|
else:
|
|
loss = (loss * mse_loss_weights).mean()
|
|
|
|
# Gather the losses across all processes for logging (if we use distributed training).
|
|
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
|
|
progress_info.train_loss += avg_loss.detach().item() / args.gradient_accumulation_steps
|
|
|
|
# Backpropagate
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = model.parameters()
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
if accelerator.sync_gradients:
|
|
sync_gradients_info(loss)
|
|
|
|
if accelerator.is_main_process:
|
|
|
|
if progress_info.global_step % args.checkpointing_steps == 0:
|
|
|
|
if args.enable_tracker:
|
|
log_validation(args, model, ae, text_enc.text_enc, train_dataset.tokenizer, accelerator,
|
|
weight_dtype, progress_info.global_step)
|
|
|
|
if args.use_ema:
|
|
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
|
|
ema_model.store(model.parameters())
|
|
ema_model.copy_to(model.parameters())
|
|
log_validation(args, model, ae, text_enc.text_enc, train_dataset.tokenizer, accelerator,
|
|
weight_dtype, progress_info.global_step, ema=True)
|
|
# Switch back to the original UNet parameters.
|
|
ema_model.restore(model.parameters())
|
|
|
|
if prof is not None:
|
|
prof.step()
|
|
|
|
|
|
return loss
|
|
|
|
def train_one_step(step_, data_item_, prof_=None):
|
|
train_loss = 0.0
|
|
x, attn_mask, input_ids, cond_mask = data_item_
|
|
if args.group_frame or args.group_resolution:
|
|
if not args.group_frame:
|
|
each_latent_frame = torch.any(attn_mask.flatten(-2), dim=-1).int().sum(-1).tolist()
|
|
# logger.info(f'rank: {accelerator.process_index}, step {step_}, special batch has attention_mask '
|
|
# f'each_latent_frame: {each_latent_frame}')
|
|
print(f'rank: {accelerator.process_index}, step {step_}, special batch has attention_mask '
|
|
f'each_latent_frame: {each_latent_frame}')
|
|
assert not torch.any(torch.isnan(x)), 'torch.any(torch.isnan(x))'
|
|
x = x.to(accelerator.device, dtype=ae.vae.dtype) # B C T+num_images H W, 16 + 4
|
|
|
|
attn_mask = attn_mask.to(accelerator.device) # B T+num_images H W
|
|
input_ids = input_ids.to(accelerator.device) # B 1+num_images L
|
|
cond_mask = cond_mask.to(accelerator.device) # B 1+num_images L
|
|
# if accelerator.process_index == 0:
|
|
# logger.info(f'rank: {accelerator.process_index}, x: {x.shape}, attn_mask: {attn_mask.shape}')
|
|
|
|
with torch.no_grad():
|
|
B, N, L = input_ids.shape # B 1+num_images L
|
|
# use batch inference
|
|
input_ids_ = input_ids.reshape(-1, L)
|
|
cond_mask_ = cond_mask.reshape(-1, L)
|
|
cond = text_enc(input_ids_, cond_mask_) # B 1+num_images L D
|
|
cond = cond.reshape(B, N, L, -1)
|
|
# Map input images to latent space + normalize latents
|
|
x = ae.encode(x) # B C T H W
|
|
|
|
# def custom_to_video(x: torch.Tensor, fps: float = 2.0, output_file: str = 'output_video.mp4') -> None:
|
|
# from examples.rec_video import array_to_video
|
|
# x = x.detach().cpu()
|
|
# x = torch.clamp(x, -1, 1)
|
|
# x = (x + 1) / 2
|
|
# x = x.permute(1, 2, 3, 0).numpy()
|
|
# x = (255*x).astype(np.uint8)
|
|
# array_to_video(x, fps=fps, output_file=output_file)
|
|
# return
|
|
# videos = ae.decode(x)[0]
|
|
# videos = videos.transpose(0, 1)
|
|
# custom_to_video(videos.to(torch.float32), fps=24, output_file='tmp.mp4')
|
|
# import sys;sys.exit()
|
|
current_step_frame = x.shape[2]
|
|
current_step_sp_state = get_sequence_parallel_state()
|
|
if args.sp_size != 1: # enable sp
|
|
if current_step_frame == 1: # but image do not need sp
|
|
set_sequence_parallel_state(False)
|
|
else:
|
|
set_sequence_parallel_state(True)
|
|
if get_sequence_parallel_state():
|
|
x, cond, attn_mask, cond_mask, use_image_num = prepare_parallel_data(x, cond, attn_mask, cond_mask,
|
|
args.use_image_num)
|
|
for iter in range(args.train_batch_size * args.sp_size // args.train_sp_batch_size):
|
|
with accelerator.accumulate(model):
|
|
st_idx = iter * args.train_sp_batch_size
|
|
ed_idx = (iter + 1) * args.train_sp_batch_size
|
|
model_kwargs = dict(encoder_hidden_states=cond[st_idx: ed_idx],
|
|
attention_mask=attn_mask[st_idx: ed_idx],
|
|
encoder_attention_mask=cond_mask[st_idx: ed_idx], use_image_num=use_image_num)
|
|
run(x[st_idx: ed_idx], model_kwargs, prof_)
|
|
|
|
else:
|
|
with accelerator.accumulate(model):
|
|
assert not torch.any(torch.isnan(x)), 'after vae'
|
|
x = x.to(weight_dtype)
|
|
model_kwargs = dict(encoder_hidden_states=cond, attention_mask=attn_mask,
|
|
encoder_attention_mask=cond_mask, use_image_num=args.use_image_num)
|
|
run(x, model_kwargs, prof_)
|
|
|
|
set_sequence_parallel_state(current_step_sp_state) # in case the next step use sp, which need broadcast(timesteps)
|
|
|
|
if progress_info.global_step >= args.max_train_steps:
|
|
return True
|
|
|
|
return False
|
|
|
|
def train_all_epoch(prof_=None):
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
progress_info.train_loss = 0.0
|
|
if progress_info.global_step >= args.max_train_steps:
|
|
return True
|
|
|
|
for step, data_item in enumerate(train_dataloader):
|
|
if train_one_step(step, data_item, prof_):
|
|
break
|
|
|
|
|
|
train_all_epoch()
|
|
accelerator.wait_for_everyone()
|
|
accelerator.end_training()
|
|
if get_sequence_parallel_state():
|
|
destroy_sequence_parallel_group()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# dataset & dataloader
|
|
parser.add_argument("--dataset", type=str, required=True)
|
|
parser.add_argument("--data", type=str, required='')
|
|
parser.add_argument("--sample_rate", type=int, default=1)
|
|
parser.add_argument("--train_fps", type=int, default=24)
|
|
parser.add_argument("--drop_short_ratio", type=float, default=1.0)
|
|
parser.add_argument("--speed_factor", type=float, default=1.0)
|
|
parser.add_argument("--num_frames", type=int, default=65)
|
|
parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
|
|
parser.add_argument("--max_height", type=int, default=320)
|
|
parser.add_argument("--max_width", type=int, default=240)
|
|
parser.add_argument("--use_img_from_vid", action="store_true")
|
|
parser.add_argument("--use_image_num", type=int, default=0)
|
|
parser.add_argument("--model_max_length", type=int, default=512)
|
|
parser.add_argument('--cfg', type=float, default=0.1)
|
|
parser.add_argument("--dataloader_num_workers", type=int, default=10, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.")
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parser.add_argument("--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader.")
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parser.add_argument("--group_frame", action="store_true")
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parser.add_argument("--group_resolution", action="store_true")
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# text encoder & vae & diffusion model
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parser.add_argument("--model", type=str, choices=list(Diffusion_models.keys()), default="Latte-XL/122")
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parser.add_argument('--enable_8bit_t5', action='store_true')
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|
parser.add_argument('--tile_overlap_factor', type=float, default=0.125)
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|
parser.add_argument('--enable_tiling', action='store_true')
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parser.add_argument("--attention_mode", type=str, choices=['xformers', 'math', 'flash'], default="xformers")
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|
parser.add_argument('--use_rope', action='store_true')
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parser.add_argument('--interpolation_scale_h', type=float, default=1.0)
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parser.add_argument('--interpolation_scale_w', type=float, default=1.0)
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parser.add_argument('--interpolation_scale_t', type=float, default=1.0)
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parser.add_argument("--downsampler", type=str, default=None)
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parser.add_argument("--ae", type=str, default="stabilityai/sd-vae-ft-mse")
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|
parser.add_argument("--ae_path", type=str, default="stabilityai/sd-vae-ft-mse")
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|
parser.add_argument("--text_encoder_name", type=str, default='DeepFloyd/t5-v1_1-xxl')
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|
parser.add_argument("--cache_dir", type=str, default='./cache_dir')
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|
parser.add_argument("--pretrained", type=str, default=None)
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|
parser.add_argument('--enable_stable_fp32', action='store_true')
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parser.add_argument("--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.")
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|
|
|
# diffusion setting
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parser.add_argument("--snr_gamma", type=float, default=None, help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. More details here: https://arxiv.org/abs/2303.09556.")
|
|
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
|
|
parser.add_argument("--ema_decay", type=float, default=0.999)
|
|
parser.add_argument("--ema_start_step", type=int, default=0)
|
|
parser.add_argument("--noise_offset", type=float, default=0.02, help="The scale of noise offset.")
|
|
parser.add_argument("--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.")
|
|
|
|
# validation & logs
|
|
parser.add_argument("--num_sampling_steps", type=int, default=20)
|
|
parser.add_argument('--guidance_scale', type=float, default=4.5)
|
|
parser.add_argument("--enable_tracker", action="store_true")
|
|
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
|
parser.add_argument("--output_dir", type=str, default=None, help="The output directory where the model predictions and checkpoints will be written.")
|
|
parser.add_argument("--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."))
|
|
parser.add_argument("--checkpointing_steps", type=int, default=500,
|
|
help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
|
|
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
|
|
" training using `--resume_from_checkpoint`."
|
|
),
|
|
)
|
|
parser.add_argument("--resume_from_checkpoint", type=str, default=None,
|
|
help=(
|
|
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
|
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
|
|
),
|
|
)
|
|
parser.add_argument("--logging_dir", type=str, default="logs",
|
|
help=(
|
|
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
|
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
|
),
|
|
)
|
|
parser.add_argument("--report_to", type=str, default="tensorboard",
|
|
help=(
|
|
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
|
|
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
|
|
),
|
|
)
|
|
# optimizer & scheduler
|
|
parser.add_argument("--num_train_epochs", type=int, default=100)
|
|
parser.add_argument("--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.")
|
|
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.")
|
|
parser.add_argument("--optimizer", type=str, default="adamW", help='The optimizer type to use. Choose between ["AdamW", "prodigy"]')
|
|
parser.add_argument("--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.")
|
|
parser.add_argument("--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.")
|
|
parser.add_argument("--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler.")
|
|
parser.add_argument("--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW")
|
|
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers.")
|
|
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers.")
|
|
parser.add_argument("--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay")
|
|
parser.add_argument("--adam_weight_decay", type=float, default=1e-02, help="Weight decay to use for unet params")
|
|
parser.add_argument("--adam_weight_decay_text_encoder", type=float, default=None, help="Weight decay to use for text_encoder")
|
|
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer and Prodigy optimizers.")
|
|
parser.add_argument("--prodigy_use_bias_correction", type=bool, default=True, help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW")
|
|
parser.add_argument("--prodigy_safeguard_warmup", type=bool, default=True, help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. Ignored if optimizer is adamW")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument("--prodigy_beta3", type=float, default=None,
|
|
help="coefficients for computing the Prodidy stepsize using running averages. If set to None, "
|
|
"uses the value of square root of beta2. Ignored if optimizer is adamW",
|
|
)
|
|
parser.add_argument("--lr_scheduler", type=str, default="constant",
|
|
help=(
|
|
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
|
|
' "constant", "constant_with_warmup"]'
|
|
),
|
|
)
|
|
parser.add_argument("--allow_tf32", action="store_true",
|
|
help=(
|
|
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
|
|
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
|
|
),
|
|
)
|
|
parser.add_argument("--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"],
|
|
help=(
|
|
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
|
|
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
|
|
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
|
|
),
|
|
)
|
|
|
|
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
parser.add_argument("--sp_size", type=int, default=1, help="For sequence parallel")
|
|
parser.add_argument("--train_sp_batch_size", type=int, default=1, help="Batch size for sequence parallel training")
|
|
|
|
args = parser.parse_args()
|
|
main(args)
|