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One of the scripts in the examples/ folder of Accelerate or an officially supported no_trainer script in the examples folder of the transformers repo (such as run_no_trainer_glue.py)
#!/usr/bin/env python# coding=utf-8# Copyright 2024 The HuggingFace Inc. team. All rights reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.importargparseimportloggingimportmathimportosimportrandomimportshutilimportsysfromcontextlibimportnullcontextfrompathlibimportPathimportaccelerateimportdatasetsimportnumpyasnpimporttorchimporttorch.nn.functionalasFimporttorch.utils.checkpointimporttransformersfromaccelerateimportAcceleratorfromaccelerate.loggingimportget_loggerfromaccelerate.stateimportAcceleratorStatefromaccelerate.utilsimportProjectConfiguration, set_seedfromdatasetsimportload_datasetfromhuggingface_hubimportcreate_repo, upload_folderfrompackagingimportversionfromtorchvisionimporttransformsfromtqdm.autoimporttqdmfromtransformersimportCLIPTextModel, CLIPTokenizerfromtransformers.utilsimportContextManagersfromaccelerate.utilsimportDeepSpeedPlugin, get_active_deepspeed_pluginimportdiffusersfromdiffusersimportAutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModelfromdiffusers.optimizationimportget_schedulerfromdiffusers.training_utilsimportEMAModel, compute_dream_and_update_latents, compute_snrfromdiffusers.utilsimportcheck_min_version, deprecate, is_wandb_available, make_image_gridfromdiffusers.utils.hub_utilsimportload_or_create_model_card, populate_model_cardfromdiffusers.utils.import_utilsimportis_xformers_availablefromdiffusers.utils.torch_utilsimportis_compiled_moduleifis_wandb_available():
importwandb# Will error if the minimal version of diffusers is not installed. Remove at your own risks.# check_min_version("0.31.0.dev0")logger=get_logger(__name__, log_level="INFO")
DATASET_NAME_MAPPING= {
"lambdalabs/naruto-blip-captions": ("image", "text"),
}
defsave_model_card(
args,
repo_id: str,
images: list=None,
repo_folder: str=None,
):
img_str=""iflen(images) >0:
image_grid=make_image_grid(images, 1, len(args.validation_prompts))
image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
img_str+="![val_imgs_grid](./val_imgs_grid.png)\n"model_description=f"""# Text-to-image finetuning - {repo_id}This pipeline was finetuned from **{args.pretrained_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n{img_str}## Pipeline usageYou can use the pipeline like so:## Training infoThese are the key hyperparameters used during training:* Epochs: {args.num_train_epochs}* Learning rate: {args.learning_rate}* Batch size: {args.train_batch_size}* Gradient accumulation steps: {args.gradient_accumulation_steps}* Image resolution: {args.resolution}* Mixed-precision: {args.mixed_precision}"""wandb_info=""ifis_wandb_available():
wandb_run_url=Noneifwandb.runisnotNone:
wandb_run_url=wandb.run.urlifwandb_run_urlisnotNone:
wandb_info=f"""More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url})."""model_description+=wandb_infomodel_card=load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="creativeml-openrail-m",
base_model=args.pretrained_model_name_or_path,
model_description=model_description,
inference=True,
)
tags= ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training"]
model_card=populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
deflog_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):
logger.info("Running validation... ")
pipeline=StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=accelerator.unwrap_model(vae),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
unet=accelerator.unwrap_model(unet),
safety_checker=None,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline=pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
ifargs.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
ifargs.seedisNone:
generator=Noneelse:
generator=torch.Generator(device=accelerator.device).manual_seed(args.seed)
images= []
foriinrange(len(args.validation_prompts)):
iftorch.backends.mps.is_available():
autocast_ctx=nullcontext()
else:
autocast_ctx=torch.autocast(accelerator.device.type)
withautocast_ctx:
image=pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
fortrackerinaccelerator.trackers:
iftracker.name=="tensorboard":
np_images=np.stack([np.asarray(img) forimginimages])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
eliftracker.name=="wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
fori, imageinenumerate(images)
]
}
)
else:
logger.warning(f"image logging not implemented for {tracker.name}")
delpipelinetorch.cuda.empty_cache()
returnimagesdefparse_args():
parser=argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this ""value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
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(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
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_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(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
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(
"--dream_training",
action="store_true",
help=(
"Use the DREAM training method, which makes training more efficient and accurate at the ",
"expense of doing an extra forward pass. See: https://arxiv.org/abs/2312.00210",
),
)
parser.add_argument(
"--dream_detail_preservation",
type=float,
default=1.0,
help="Dream detail preservation factor p (should be greater than 0; default=1.0, as suggested in the paper)",
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
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("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument("--offload_ema", action="store_true", help="Offload EMA model to CPU during training step.")
parser.add_argument("--foreach_ema", action="store_true", help="Use faster foreach implementation of EMAModel.")
parser.add_argument(
"--non_ema_revision",
type=str,
default=None,
required=False,
help=(
"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"" remote repository specified with --pretrained_model_name_or_path."
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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.prediction_type` is chosen.",
)
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
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(
"--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(
"--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.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
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(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args=parser.parse_args()
env_local_rank=int(os.environ.get("LOCAL_RANK", -1))
ifenv_local_rank!=-1andenv_local_rank!=args.local_rank:
args.local_rank=env_local_rank# Sanity checksifargs.dataset_nameisNoneandargs.train_data_dirisNone:
raiseValueError("Need either a dataset name or a training folder.")
# default to using the same revision for the non-ema model if not specifiedifargs.non_ema_revisionisNone:
args.non_ema_revision=args.revisionreturnargsdefmain():
args=parse_args()
ifargs.report_to=="wandb"andargs.hub_tokenisnotNone:
raiseValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."" Please use `huggingface-cli login` to authenticate with the Hub."
)
ifargs.non_ema_revisionisnotNone:
deprecate(
"non_ema_revision!=None",
"0.15.0",
message=(
"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"" use `--variant=non_ema` instead."
),
)
logging_dir=os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config=ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
deepspeed_plugins= {
"train": DeepSpeedPlugin(hf_ds_config="script/train-t2i-sd-ds-config.json"),
"infer_only_text_encoder": DeepSpeedPlugin(hf_ds_config="script/train-t2i-sd-ds-config-infer-only.json"),
"infer_only_vae": DeepSpeedPlugin(hf_ds_config="script/train-t2i-sd-ds-config-infer-only.json"),
}
accelerator=Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
deepspeed_plugins=deepspeed_plugins,
)
# Disable AMP for MPS.iftorch.backends.mps.is_available():
accelerator.native_amp=False# Make one log on every process with the configuration for debugging.logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
ifaccelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.ifargs.seedisnotNone:
set_seed(args.seed)
# Handle the repository creationifaccelerator.is_main_process:
ifargs.output_dirisnotNone:
os.makedirs(args.output_dir, exist_ok=True)
ifargs.push_to_hub:
repo_id=create_repo(
repo_id=args.hub_model_idorPath(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id# Load scheduler, tokenizer and models.noise_scheduler=DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
tokenizer=CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
defdeepspeed_zero_init_disabled_context_manager():
""" returns either a context list that includes one that will disable zero.Init or an empty context list """deepspeed_plugin=AcceleratorState().deepspeed_pluginifaccelerate.state.is_initialized() elseNoneifdeepspeed_pluginisNone:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
ifversion.parse(accelerate.__version__) >=version.parse("1.0.1"):
# Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.# For this to work properly all models must be run through `accelerate.prepare`. But accelerate# will try to assign the same optimizer with the same weights to all models during# `deepspeed.initialize`, which of course doesn't work.## For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2# frozen models from being partitioned during `zero.Init` which gets called during# `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding# across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.withContextManagers(deepspeed_zero_init_disabled_context_manager()):
text_encoder=CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
vae=AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
unet=UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
)
else:
accelerator.state.select_deepspeed_plugin("infer_only_text_encoder")
text_encoder=CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder=accelerator.prepare(text_encoder)
accelerator.state.select_deepspeed_plugin("infer_only_vae")
vae=AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
vae=accelerator.prepare(vae)
accelerator.state.select_deepspeed_plugin("train")
unet=UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
)
# Freeze vae and text_encoder and set unet to trainablevae.requires_grad_(False)
text_encoder.requires_grad_(False)
unet.train()
# Create EMA for the unet.ifargs.use_ema:
ema_unet=UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
ema_unet=EMAModel(
ema_unet.parameters(),
model_cls=UNet2DConditionModel,
model_config=ema_unet.config,
foreach=args.foreach_ema,
)
ifargs.enable_xformers_memory_efficient_attention:
ifis_xformers_available():
importxformersxformers_version=version.parse(xformers.__version__)
ifxformers_version==version.parse("0.0.16"):
logger.warning(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raiseValueError("xformers is not available. Make sure it is installed correctly")
# `accelerate` 0.16.0 will have better support for customized savingifversion.parse(accelerate.__version__) >=version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice formatdefsave_model_hook(models, weights, output_dir):
ifaccelerator.is_main_process:
ifargs.use_ema:
ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
fori, modelinenumerate(models):
model.save_pretrained(os.path.join(output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved againweights.pop()
defload_model_hook(models, input_dir):
ifargs.use_ema:
load_model=EMAModel.from_pretrained(
os.path.join(input_dir, "unet_ema"), UNet2DConditionModel, foreach=args.foreach_ema
)
ema_unet.load_state_dict(load_model.state_dict())
ifargs.offload_ema:
ema_unet.pin_memory()
else:
ema_unet.to(accelerator.device)
delload_modelfor_inrange(len(models)):
# pop models so that they are not loaded againmodel=models.pop()
# load diffusers style into modelload_model=UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
delload_modelaccelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
ifargs.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# Enable TF32 for faster training on Ampere GPUs,# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devicesifargs.allow_tf32:
torch.backends.cuda.matmul.allow_tf32=Trueifargs.scale_lr:
args.learning_rate= (
args.learning_rate*args.gradient_accumulation_steps*args.train_batch_size*accelerator.num_processes
)
# Initialize the optimizerifargs.use_8bit_adam:
try:
importbitsandbytesasbnbexceptImportError:
raiseImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls=bnb.optim.AdamW8bitelse:
optimizer_cls=torch.optim.AdamWoptimizer=optimizer_cls(
unet.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).# In distributed training, the load_dataset function guarantees that only one local process can concurrently# download the dataset.ifargs.dataset_nameisnotNone:
# Downloading and loading a dataset from the hub.dataset=load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
data_dir=args.train_data_dir,
)
else:
data_files= {}
ifargs.train_data_dirisnotNone:
data_files["train"] =os.path.join(args.train_data_dir, "**")
dataset=load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder# Preprocessing the datasets.# We need to tokenize inputs and targets.column_names=dataset["train"].column_names# 6. Get the column names for input/target.dataset_columns=DATASET_NAME_MAPPING.get(args.dataset_name, None)
ifargs.image_columnisNone:
image_column=dataset_columns[0] ifdataset_columnsisnotNoneelsecolumn_names[0]
else:
image_column=args.image_columnifimage_columnnotincolumn_names:
raiseValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
ifargs.caption_columnisNone:
caption_column=dataset_columns[1] ifdataset_columnsisnotNoneelsecolumn_names[1]
else:
caption_column=args.caption_columnifcaption_columnnotincolumn_names:
raiseValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.# We need to tokenize input captions and transform the images.deftokenize_captions(examples, is_train=True):
captions= []
forcaptioninexamples[caption_column]:
ifisinstance(caption, str):
captions.append(caption)
elifisinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiplecaptions.append(random.choice(caption) ifis_trainelsecaption[0])
else:
raiseValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs=tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
returninputs.input_ids# Preprocessing the datasets.train_transforms=transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) ifargs.center_cropelsetransforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() ifargs.random_flipelsetransforms.Lambda(lambdax: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
defpreprocess_train(examples):
images= [image.convert("RGB") forimageinexamples[image_column]]
examples["pixel_values"] = [train_transforms(image) forimageinimages]
examples["input_ids"] =tokenize_captions(examples)
returnexampleswithaccelerator.main_process_first():
ifargs.max_train_samplesisnotNone:
dataset["train"] =dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transformstrain_dataset=dataset["train"].with_transform(preprocess_train)
defcollate_fn(examples):
pixel_values=torch.stack([example["pixel_values"] forexampleinexamples])
pixel_values=pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids=torch.stack([example["input_ids"] forexampleinexamples])
return {"pixel_values": pixel_values, "input_ids": input_ids}
# DataLoaders creation:train_dataloader=torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.# Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.num_warmup_steps_for_scheduler=args.lr_warmup_steps*accelerator.num_processesifargs.max_train_stepsisNone:
len_train_dataloader_after_sharding=math.ceil(len(train_dataloader) /accelerator.num_processes)
num_update_steps_per_epoch=math.ceil(len_train_dataloader_after_sharding/args.gradient_accumulation_steps)
num_training_steps_for_scheduler= (
args.num_train_epochs*num_update_steps_per_epoch*accelerator.num_processes
)
else:
num_training_steps_for_scheduler=args.max_train_steps*accelerator.num_processeslr_scheduler=get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=num_warmup_steps_for_scheduler,
num_training_steps=num_training_steps_for_scheduler,
)
# Prepare everything with our `accelerator`.unet, optimizer, train_dataloader, lr_scheduler=accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
ifargs.use_ema:
ifargs.offload_ema:
ema_unet.pin_memory()
else:
ema_unet.to(accelerator.device)
# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision# as these weights are only used for inference, keeping weights in full precision is not required.weight_dtype=torch.float32ifaccelerator.mixed_precision=="fp16":
weight_dtype=torch.float16args.mixed_precision=accelerator.mixed_precisionelifaccelerator.mixed_precision=="bf16":
weight_dtype=torch.bfloat16args.mixed_precision=accelerator.mixed_precision# Move text_encode and vae to gpu and cast to weight_dtypetext_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.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) /args.gradient_accumulation_steps)
ifargs.max_train_stepsisNone:
args.max_train_steps=args.num_train_epochs*num_update_steps_per_epochifnum_training_steps_for_scheduler!=args.max_train_steps*accelerator.num_processes:
logger.warning(
f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "f"This inconsistency may result in the learning rate scheduler not functioning properly."
)
# Afterwards we recalculate our number of training epochsargs.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.ifaccelerator.is_main_process:
tracker_config=dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Function for unwrapping if model was compiled with `torch.compile`.defunwrap_model(model):
assertFalsemodel=accelerator.unwrap_model(model)
model=model._orig_modifis_compiled_module(model) elsemodelreturnmodel# Train!total_batch_size=args.train_batch_size*accelerator.num_processes*args.gradient_accumulation_stepslogger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step=0first_epoch=0# Potentially load in the weights and states from a previous saveifargs.resume_from_checkpoint:
ifargs.resume_from_checkpoint!="latest":
path=os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpointdirs=os.listdir(args.output_dir)
dirs= [dfordindirsifd.startswith("checkpoint")]
dirs=sorted(dirs, key=lambdax: int(x.split("-")[1]))
path=dirs[-1] iflen(dirs) >0elseNoneifpathisNone:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint=Noneinitial_global_step=0else:
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_stepfirst_epoch=global_step//num_update_steps_per_epochelse:
initial_global_step=0progress_bar=tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.disable=notaccelerator.is_local_main_process,
)
forepochinrange(first_epoch, args.num_train_epochs):
train_loss=0.0forstep, batchinenumerate(train_dataloader):
withaccelerator.accumulate(unet):
# Convert images to latent spacelatents=vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample()
latents=latents*vae.config.scaling_factor# Sample noise that we'll add to the latentsnoise=torch.randn_like(latents)
ifargs.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noisenoise+=args.noise_offset*torch.randn(
(latents.shape[0], latents.shape[1], 1, 1), device=latents.device
)
ifargs.input_perturbation:
new_noise=noise+args.input_perturbation*torch.randn_like(noise)
bsz=latents.shape[0]
# Sample a random timestep for each imagetimesteps=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)ifargs.input_perturbation:
noisy_latents=noise_scheduler.add_noise(latents, new_noise, timesteps)
else:
noisy_latents=noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioningencoder_hidden_states=text_encoder(batch["input_ids"], return_dict=False)[0]
# Get the target for loss depending on the prediction typeifargs.prediction_typeisnotNone:
# set prediction_type of scheduler if definednoise_scheduler.register_to_config(prediction_type=args.prediction_type)
ifnoise_scheduler.config.prediction_type=="epsilon":
target=noiseelifnoise_scheduler.config.prediction_type=="v_prediction":
target=noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raiseValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
ifargs.dream_training:
noisy_latents, target=compute_dream_and_update_latents(
unet,
noise_scheduler,
timesteps,
noise,
noisy_latents,
target,
encoder_hidden_states,
args.dream_detail_preservation,
)
# Predict the noise residual and compute lossmodel_pred=unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]
ifargs.snr_gammaisNone:
loss=F.mse_loss(model_pred.float(), target.float(), reduction="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]
ifnoise_scheduler.config.prediction_type=="epsilon":
mse_loss_weights=mse_loss_weights/snrelifnoise_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.mean(dim=list(range(1, len(loss.shape)))) *mse_loss_weightsloss=loss.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()
train_loss+=avg_loss.item() /args.gradient_accumulation_steps# Backpropagateaccelerator.backward(loss)
ifaccelerator.sync_gradients:
accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenesifaccelerator.sync_gradients:
ifargs.use_ema:
ifargs.offload_ema:
ema_unet.to(device="cuda", non_blocking=True)
ema_unet.step(unet.parameters())
ifargs.offload_ema:
ema_unet.to(device="cpu", non_blocking=True)
progress_bar.update(1)
global_step+=1accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss=0.0ifglobal_step%args.checkpointing_steps==0:
ifaccelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`ifargs.checkpoints_total_limitisnotNone:
checkpoints=os.listdir(args.output_dir)
checkpoints= [dfordincheckpointsifd.startswith("checkpoint")]
checkpoints=sorted(checkpoints, key=lambdax: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpointsiflen(checkpoints) >=args.checkpoints_total_limit:
num_to_remove=len(checkpoints) -args.checkpoints_total_limit+1removing_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)}")
forremoving_checkpointinremoving_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-{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)
ifglobal_step>=args.max_train_steps:
breakifaccelerator.is_main_process:
ifargs.validation_promptsisnotNoneandepoch%args.validation_epochs==0:
ifargs.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.ema_unet.store(unet.parameters())
ema_unet.copy_to(unet.parameters())
log_validation(
vae,
text_encoder,
tokenizer,
unet,
args,
accelerator,
weight_dtype,
global_step,
)
ifargs.use_ema:
# Switch back to the original UNet parameters.ema_unet.restore(unet.parameters())
# Create the pipeline using the trained modules and save it.accelerator.wait_for_everyone()
ifaccelerator.is_main_process:
unet=unwrap_model(unet)
ifargs.use_ema:
ema_unet.copy_to(unet.parameters())
pipeline=StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=text_encoder,
vae=vae,
unet=unet,
revision=args.revision,
variant=args.variant,
)
pipeline.save_pretrained(args.output_dir)
# Run a final round of inference.images= []
ifargs.validation_promptsisnotNone:
logger.info("Running inference for collecting generated images...")
pipeline=pipeline.to(accelerator.device)
pipeline.torch_dtype=weight_dtypepipeline.set_progress_bar_config(disable=True)
ifargs.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
ifargs.seedisNone:
generator=Noneelse:
generator=torch.Generator(device=accelerator.device).manual_seed(args.seed)
foriinrange(len(args.validation_prompts)):
withtorch.autocast("cuda"):
image=pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
ifargs.push_to_hub:
save_model_card(args, repo_id, images, repo_folder=args.output_dir)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if__name__=="__main__":
main()
I saw your commits in git history, so maybe I @ ed you , maybe you can kindly help?
I believe using multiple model and zero-3 in diffusion training is quite important (for larger model like FLUX), it's a critical ability of Accelerate.
nrailg
changed the title
Accelerate 1.0.1 failed to train multiple zero-3 models
[BUG] Accelerate 1.0.1 failed to train multiple zero-3 models
Oct 15, 2024
System Info
Information
Tasks
no_trainer
script in theexamples
folder of thetransformers
repo (such asrun_no_trainer_glue.py
)Reproduction
Code below copied from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
I made several modifications according to https://github.com/huggingface/accelerate/blob/main/docs/source/usage_guides/deepspeed_multiple_model.md
My configs
script/train-t2i-sd-ds-config.json
script/train-t2i-sd-ds-config-infer-only.json
How I ran the code
Expected behavior
What I got
If i change zero-3 to zero-1 in
script/train-t2i-sd-ds-config.json
, it works well.The text was updated successfully, but these errors were encountered: