mirror of https://github.com/hpcaitech/ColossalAI
719 lines
29 KiB
Python
719 lines
29 KiB
Python
import argparse
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import hashlib
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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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from typing import Optional
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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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from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
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from diffusers.loaders import AttnProcsLayers
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from diffusers.models.cross_attention import LoRACrossAttnProcessor
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from diffusers.optimization import get_scheduler
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from huggingface_hub import HfFolder, Repository, create_repo, whoami
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from PIL import Image
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from torch.utils.data import Dataset
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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, PretrainedConfig
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.logging import disable_existing_loggers, get_dist_logger
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.utils import get_current_device
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from colossalai.zero import ColoInitContext, GeminiAdamOptimizer
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from colossalai.zero.gemini import get_static_torch_model
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disable_existing_loggers()
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logger = get_dist_logger()
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def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
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text_encoder_config = PretrainedConfig.from_pretrained(
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pretrained_model_name_or_path,
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subfolder="text_encoder",
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revision=args.revision,
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)
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model_class = text_encoder_config.architectures[0]
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if model_class == "CLIPTextModel":
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from transformers import CLIPTextModel
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return CLIPTextModel
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elif model_class == "RobertaSeriesModelWithTransformation":
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from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
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return RobertaSeriesModelWithTransformation
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else:
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raise ValueError(f"{model_class} is not supported.")
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def parse_args(input_args=None):
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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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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"--externel_unet_path",
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type=str,
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default=None,
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required=False,
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help="Path to the externel unet model.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--instance_data_dir",
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type=str,
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default=None,
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required=True,
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help="A folder containing the training data of instance images.",
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)
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parser.add_argument(
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"--class_data_dir",
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type=str,
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default=None,
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required=False,
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help="A folder containing the training data of class images.",
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)
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parser.add_argument(
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"--instance_prompt",
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type=str,
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default="a photo of sks dog",
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required=False,
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help="The prompt with identifier specifying the instance",
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)
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parser.add_argument(
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"--class_prompt",
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type=str,
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default=None,
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help="The prompt to specify images in the same class as provided instance images.",
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)
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parser.add_argument(
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"--with_prior_preservation",
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default=False,
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action="store_true",
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help="Flag to add prior preservation loss.",
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)
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parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
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parser.add_argument(
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"--num_class_images",
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type=int,
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default=100,
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help=("Minimal class images for prior preservation loss. If there are not enough images already present in"
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" class_data_dir, additional images will be sampled with class_prompt."),
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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="text-inversion-model",
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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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"--resolution",
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type=int,
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default=512,
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help=("The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"),
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)
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parser.add_argument(
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"--placement",
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type=str,
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default="cpu",
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help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
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)
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parser.add_argument(
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"--center_crop",
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default=False,
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action="store_true",
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help=("Whether to center crop the input images to the resolution. If not set, the images will be randomly"
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" cropped. The images will be resized to the resolution first before cropping."),
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)
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parser.add_argument("--train_batch_size",
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type=int,
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default=4,
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help="Batch size (per device) for the training dataloader.")
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parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.")
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parser.add_argument("--num_train_epochs", type=int, default=1)
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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("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
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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=5e-6,
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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=('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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parser.add_argument("--lr_warmup_steps",
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type=int,
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default=500,
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help="Number of steps for the warmup in the lr scheduler.")
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parser.add_argument("--use_8bit_adam",
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action="store_true",
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help="Whether or not to use 8-bit Adam from bitsandbytes.")
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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("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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parser.add_argument('-p',
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'--plugin',
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type=str,
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default='torch_ddp',
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choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'],
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help="plugin to use")
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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=("[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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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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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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if input_args is not None:
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args = parser.parse_args(input_args)
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else:
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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if args.with_prior_preservation:
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if args.class_data_dir is None:
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raise ValueError("You must specify a data directory for class images.")
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if args.class_prompt is None:
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raise ValueError("You must specify prompt for class images.")
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else:
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if args.class_data_dir is not None:
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logger.warning("You need not use --class_data_dir without --with_prior_preservation.")
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if args.class_prompt is not None:
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logger.warning("You need not use --class_prompt without --with_prior_preservation.")
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return args
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class DreamBoothDataset(Dataset):
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"""
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A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
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It pre-processes the images and the tokenizes prompts.
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"""
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def __init__(
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self,
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instance_data_root,
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instance_prompt,
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tokenizer,
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class_data_root=None,
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class_prompt=None,
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size=512,
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center_crop=False,
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):
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self.size = size
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self.center_crop = center_crop
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self.tokenizer = tokenizer
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self.instance_data_root = Path(instance_data_root)
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if not self.instance_data_root.exists():
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raise ValueError("Instance images root doesn't exists.")
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self.instance_images_path = list(Path(instance_data_root).iterdir())
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self.num_instance_images = len(self.instance_images_path)
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self.instance_prompt = instance_prompt
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self._length = self.num_instance_images
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if class_data_root is not None:
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self.class_data_root = Path(class_data_root)
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self.class_data_root.mkdir(parents=True, exist_ok=True)
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self.class_images_path = list(self.class_data_root.iterdir())
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self.num_class_images = len(self.class_images_path)
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self._length = max(self.num_class_images, self.num_instance_images)
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self.class_prompt = class_prompt
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else:
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self.class_data_root = None
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self.image_transforms = transforms.Compose([
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transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
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transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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])
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def __len__(self):
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return self._length
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def __getitem__(self, index):
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example = {}
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instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
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if not instance_image.mode == "RGB":
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instance_image = instance_image.convert("RGB")
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example["instance_images"] = self.image_transforms(instance_image)
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example["instance_prompt_ids"] = self.tokenizer(
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self.instance_prompt,
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padding="do_not_pad",
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truncation=True,
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max_length=self.tokenizer.model_max_length,
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).input_ids
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if self.class_data_root:
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class_image = Image.open(self.class_images_path[index % self.num_class_images])
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if not class_image.mode == "RGB":
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class_image = class_image.convert("RGB")
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example["class_images"] = self.image_transforms(class_image)
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example["class_prompt_ids"] = self.tokenizer(
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self.class_prompt,
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padding="do_not_pad",
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truncation=True,
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max_length=self.tokenizer.model_max_length,
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).input_ids
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return example
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class PromptDataset(Dataset):
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"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
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def __init__(self, prompt, num_samples):
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self.prompt = prompt
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self.num_samples = num_samples
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def __len__(self):
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return self.num_samples
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def __getitem__(self, index):
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example = {}
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example["prompt"] = self.prompt
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example["index"] = index
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return example
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def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
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if token is None:
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token = HfFolder.get_token()
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if organization is None:
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username = whoami(token)["name"]
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return f"{username}/{model_id}"
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else:
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return f"{organization}/{model_id}"
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def main(args):
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if args.seed is None:
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colossalai.launch_from_torch(config={})
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else:
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colossalai.launch_from_torch(config={}, seed=args.seed)
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local_rank = gpc.get_local_rank(ParallelMode.DATA)
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world_size = gpc.get_world_size(ParallelMode.DATA)
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if args.with_prior_preservation:
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class_images_dir = Path(args.class_data_dir)
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if not class_images_dir.exists():
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class_images_dir.mkdir(parents=True)
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cur_class_images = len(list(class_images_dir.iterdir()))
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if cur_class_images < args.num_class_images:
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torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32
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pipeline = DiffusionPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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torch_dtype=torch_dtype,
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safety_checker=None,
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revision=args.revision,
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)
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pipeline.set_progress_bar_config(disable=True)
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num_new_images = args.num_class_images - cur_class_images
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logger.info(f"Number of class images to sample: {num_new_images}.")
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sample_dataset = PromptDataset(args.class_prompt, num_new_images)
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sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
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pipeline.to(get_current_device())
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for example in tqdm(
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sample_dataloader,
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desc="Generating class images",
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disable=not local_rank == 0,
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):
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images = pipeline(example["prompt"]).images
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for i, image in enumerate(images):
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hash_image = hashlib.sha256(image.tobytes()).hexdigest()
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image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
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image.save(image_filename)
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del pipeline
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# Handle the repository creation
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if local_rank == 0:
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if args.push_to_hub:
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if args.hub_model_id is None:
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repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
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else:
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repo_name = args.hub_model_id
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create_repo(repo_name, exist_ok=True, token=args.hub_token)
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repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
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with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
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if "step_*" not in gitignore:
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gitignore.write("step_*\n")
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if "epoch_*" not in gitignore:
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gitignore.write("epoch_*\n")
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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# Load the tokenizer
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if args.tokenizer_name:
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logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0])
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tokenizer = AutoTokenizer.from_pretrained(
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args.tokenizer_name,
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revision=args.revision,
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use_fast=False,
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)
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elif args.pretrained_model_name_or_path:
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logger.info("Loading tokenizer from pretrained model", ranks=[0])
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tokenizer = AutoTokenizer.from_pretrained(
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args.pretrained_model_name_or_path,
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subfolder="tokenizer",
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revision=args.revision,
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use_fast=False,
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)
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# import correct text encoder class
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text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
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# Load models and create wrapper for stable diffusion
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logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0])
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text_encoder = text_encoder_cls.from_pretrained(
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args.pretrained_model_name_or_path,
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subfolder="text_encoder",
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revision=args.revision,
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)
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logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0])
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vae = AutoencoderKL.from_pretrained(
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args.pretrained_model_name_or_path,
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subfolder="vae",
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revision=args.revision,
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)
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if args.externel_unet_path is None:
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logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
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unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
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subfolder="unet",
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revision=args.revision,
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low_cpu_mem_usage=False)
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else:
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logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0])
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unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path,
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revision=args.revision,
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low_cpu_mem_usage=False)
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unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
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subfolder="unet",
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revision=args.revision,
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low_cpu_mem_usage=False)
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unet.requires_grad_(False)
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# Set correct lora layers
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|
lora_attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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|
elif name.startswith("down_blocks"):
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|
block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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|
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lora_attn_procs[name] = LoRACrossAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
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|
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unet.set_attn_processor(lora_attn_procs)
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lora_layers = AttnProcsLayers(unet.attn_processors)
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|
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vae.requires_grad_(False)
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text_encoder.requires_grad_(False)
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|
|
|
if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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|
|
|
if args.scale_lr:
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args.learning_rate = args.learning_rate * args.train_batch_size * world_size
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|
|
|
# Use Booster API to use Gemini/Zero with ColossalAI
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|
|
|
booster_kwargs = {}
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if args.plugin == 'torch_ddp_fp16':
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booster_kwargs['mixed_precision'] = 'fp16'
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if args.plugin.startswith('torch_ddp'):
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plugin = TorchDDPPlugin()
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elif args.plugin == 'gemini':
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plugin = GeminiPlugin(strict_ddp_mode=True, initial_scale=2**5)
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elif args.plugin == 'low_level_zero':
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plugin = LowLevelZeroPlugin(initial_scale=2**5)
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|
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|
booster = Booster(plugin=plugin, **booster_kwargs)
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|
|
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# config optimizer for colossalai zero
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|
optimizer = HybridAdam(unet.parameters(),
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lr=args.learning_rate,
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initial_scale=2**5,
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|
clipping_norm=args.max_grad_norm)
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|
|
|
# load noise_scheduler
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noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
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|
|
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# prepare dataset
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|
logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0])
|
|
train_dataset = DreamBoothDataset(
|
|
instance_data_root=args.instance_data_dir,
|
|
instance_prompt=args.instance_prompt,
|
|
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
|
|
class_prompt=args.class_prompt,
|
|
tokenizer=tokenizer,
|
|
size=args.resolution,
|
|
center_crop=args.center_crop,
|
|
)
|
|
|
|
def collate_fn(examples):
|
|
input_ids = [example["instance_prompt_ids"] for example in examples]
|
|
pixel_values = [example["instance_images"] for example in examples]
|
|
|
|
# Concat class and instance examples for prior preservation.
|
|
# We do this to avoid doing two forward passes.
|
|
if args.with_prior_preservation:
|
|
input_ids += [example["class_prompt_ids"] for example in examples]
|
|
pixel_values += [example["class_images"] for example in examples]
|
|
|
|
pixel_values = torch.stack(pixel_values)
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
input_ids = tokenizer.pad(
|
|
{
|
|
"input_ids": input_ids
|
|
},
|
|
padding="max_length",
|
|
max_length=tokenizer.model_max_length,
|
|
return_tensors="pt",
|
|
).input_ids
|
|
|
|
batch = {
|
|
"input_ids": input_ids,
|
|
"pixel_values": pixel_values,
|
|
}
|
|
return batch
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(train_dataset,
|
|
batch_size=args.train_batch_size,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
num_workers=1)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
|
|
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,
|
|
num_training_steps=args.max_train_steps,
|
|
)
|
|
weight_dtype = torch.float32
|
|
if args.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
elif args.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# Move text_encode and vae to gpu.
|
|
# For mixed precision training we cast the text_encoder and vae weights to half-precision
|
|
# as these models are only used for inference, keeping weights in full precision is not required.
|
|
vae.to(get_current_device(), dtype=weight_dtype)
|
|
text_encoder.to(get_current_device(), dtype=weight_dtype)
|
|
|
|
# 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))
|
|
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)
|
|
|
|
unet, optimizer, _, _, lr_scheduler = booster.boost(unet, optimizer, lr_scheduler=lr_scheduler)
|
|
|
|
# Train!
|
|
total_batch_size = args.train_batch_size * world_size
|
|
|
|
logger.info("***** Running training *****", ranks=[0])
|
|
logger.info(f" Num examples = {len(train_dataset)}", ranks=[0])
|
|
logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0])
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0])
|
|
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0])
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0])
|
|
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0)
|
|
progress_bar.set_description("Steps")
|
|
global_step = 0
|
|
|
|
torch.cuda.synchronize()
|
|
for epoch in range(args.num_train_epochs):
|
|
unet.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
# Move batch to gpu
|
|
for key, value in batch.items():
|
|
batch[key] = value.to(get_current_device(), non_blocking=True)
|
|
|
|
# Convert images to latent space
|
|
optimizer.zero_grad()
|
|
|
|
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * 0.18215
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
|
timesteps = timesteps.long()
|
|
|
|
# Add noise to the latents according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
|
|
|
# Predict the noise residual
|
|
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
|
|
|
# Get the target for loss depending on the prediction type
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
if args.with_prior_preservation:
|
|
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
|
|
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
|
|
target, target_prior = torch.chunk(target, 2, dim=0)
|
|
|
|
# Compute instance loss
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
|
|
|
|
# Compute prior loss
|
|
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
|
|
|
|
# Add the prior loss to the instance loss.
|
|
loss = loss + args.prior_loss_weight * prior_loss
|
|
else:
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
|
|
optimizer.backward(loss)
|
|
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0])
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
logs = {
|
|
"loss": loss.detach().item(),
|
|
"lr": optimizer.param_groups[0]["lr"],
|
|
} # lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
if global_step % args.save_steps == 0:
|
|
torch.cuda.synchronize()
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
booster.save_model(unet, os.path.join(save_path, "diffusion_pytorch_model.bin"))
|
|
if local_rank == 0:
|
|
if not os.path.exists(os.path.join(save_path, "config.json")):
|
|
shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), save_path)
|
|
logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
torch.cuda.synchronize()
|
|
|
|
booster.save_model(unet, os.path.join(args.output_dir, "diffusion_pytorch_model.bin"))
|
|
logger.info(f"Saving model checkpoint to {args.output_dir} on rank {local_rank}")
|
|
if local_rank == 0:
|
|
if not os.path.exists(os.path.join(args.output_dir, "config.json")):
|
|
shutil.copy(os.path.join(args.pretrained_model_name_or_path, "unet/config.json"), args.output_dir)
|
|
if args.push_to_hub:
|
|
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
main(args)
|