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#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2023 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 | |
import argparse | |
import logging | |
import math | |
import os | |
import warnings | |
from pathlib import Path | |
import datasets | |
import numpy as np | |
import torch | |
from accelerate import Accelerator, DistributedType | |
from accelerate.utils import set_seed | |
from datasets import load_dataset | |
from huggingface_hub import Repository, create_repo | |
from torch.utils.data import DataLoader | |
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor | |
from tqdm.auto import tqdm | |
import transformers | |
from transformers import ( | |
CONFIG_MAPPING, | |
IMAGE_PROCESSOR_MAPPING, | |
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, | |
AutoConfig, | |
AutoImageProcessor, | |
AutoModelForMaskedImageModeling, | |
SchedulerType, | |
get_scheduler, | |
) | |
from transformers.utils import check_min_version, send_example_telemetry | |
from transformers.utils.versions import require_version | |
""" Pre-training a 🤗 Transformers model for simple masked image modeling (SimMIM) | |
without using HuggingFace Trainer. | |
Any model supported by the AutoModelForMaskedImageModeling API can be used. | |
""" | |
logger = logging.getLogger(__name__) | |
# Will error if the minimal version of Transformers is not installed. Remove at your own risks. | |
check_min_version("4.34.0.dev0") | |
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt") | |
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys()) | |
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) | |
def parse_args(): | |
parser = argparse.ArgumentParser( | |
description="Finetune a transformers model on a simple Masked Image Modeling task" | |
) | |
parser.add_argument( | |
"--dataset_name", | |
type=str, | |
default="cifar10", | |
help="Name of a dataset from the datasets package", | |
) | |
parser.add_argument( | |
"--dataset_config_name", | |
type=str, | |
default=None, | |
help="The configuration name of the dataset to use (via the datasets library).", | |
) | |
parser.add_argument( | |
"--image_column_name", | |
type=str, | |
default=None, | |
help="The column name of the images in the files. If not set, will try to use 'image' or 'img'.", | |
) | |
parser.add_argument( | |
"--train_dir", | |
type=str, | |
default=None, | |
help="A folder containing the training data.", | |
) | |
parser.add_argument( | |
"--validation_dir", | |
type=None, | |
default=None, | |
help="A folder containing the validation data.", | |
) | |
parser.add_argument( | |
"--train_val_split", | |
type=float, | |
default=0.15, | |
help="Percent to split off of train for validation.", | |
) | |
parser.add_argument( | |
"--mask_patch_size", | |
type=int, | |
default=32, | |
help="The size of the square patches to use for masking.", | |
) | |
parser.add_argument( | |
"--mask_ratio", | |
type=float, | |
default=0.6, | |
help="Percentage of patches to mask.", | |
) | |
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( | |
"--max_eval_samples", | |
type=int, | |
default=None, | |
help=( | |
"For debugging purposes or quicker training, truncate the number of evaluation examples to this " | |
"value if set." | |
), | |
) | |
parser.add_argument( | |
"--model_name_or_path", | |
type=str, | |
default=None, | |
help=( | |
"The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a " | |
"checkpoint identifier on the hub. " | |
"Don't set if you want to train a model from scratch." | |
), | |
) | |
parser.add_argument( | |
"--model_type", | |
type=str, | |
default=None, | |
help="If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES), | |
) | |
parser.add_argument( | |
"--config_name_or_path", | |
type=str, | |
default=None, | |
help="Pretrained config name or path if not the same as model_name", | |
) | |
parser.add_argument( | |
"--config_overrides", | |
type=str, | |
default=None, | |
help=( | |
"Override some existing default config settings when a model is trained from scratch. Example: " | |
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" | |
), | |
) | |
parser.add_argument( | |
"--cache_dir", | |
type=str, | |
default=None, | |
help="Where do you want to store (cache) the pretrained models/datasets downloaded from the hub", | |
) | |
parser.add_argument( | |
"--model_revision", | |
type=str, | |
default="main", | |
help="The specific model version to use (can be a branch name, tag name or commit id).", | |
) | |
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( | |
"--image_processor_name", | |
type=str, | |
default=None, | |
help="Name or path of preprocessor config.", | |
) | |
parser.add_argument( | |
"--token", | |
type=str, | |
default=None, | |
help=( | |
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " | |
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)." | |
), | |
) | |
parser.add_argument( | |
"--use_auth_token", | |
type=bool, | |
default=None, | |
help="The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`.", | |
) | |
parser.add_argument( | |
"--trust_remote_code", | |
type=bool, | |
default=False, | |
help=( | |
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option" | |
"should only be set to `True` for repositories you trust and in which you have read the code, as it will" | |
"execute code present on the Hub on your local machine." | |
), | |
) | |
parser.add_argument( | |
"--image_size", | |
type=int, | |
default=None, | |
help="The size (resolution) of each image. If not specified, will use `image_size` of the configuration.", | |
) | |
parser.add_argument( | |
"--patch_size", | |
type=int, | |
default=None, | |
help="The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.", | |
) | |
parser.add_argument( | |
"--encoder_stride", | |
type=int, | |
default=None, | |
help={"help": "Stride to use for the encoder."}, | |
) | |
parser.add_argument( | |
"--push_to_hub", | |
action="store_true", | |
help="Whether or not to push the model to the Hub.", | |
) | |
parser.add_argument( | |
"--with_tracking", | |
action="store_true", | |
help="Whether to enable experiment trackers for logging.", | |
) | |
parser.add_argument( | |
"--report_to", | |
type=str, | |
default="all", | |
help=( | |
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' | |
' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' | |
"Only applicable when `--with_tracking` is passed." | |
), | |
) | |
parser.add_argument( | |
"--seed", | |
type=int, | |
default=None, | |
help="A seed for reproducible training.", | |
) | |
parser.add_argument( | |
"--per_device_train_batch_size", | |
type=int, | |
default=8, | |
help="Batch size (per device) for the training dataloader.", | |
) | |
parser.add_argument( | |
"--learning_rate", | |
type=float, | |
default=5e-5, | |
help="The initial learning rate for [`AdamW`] optimizer.", | |
) | |
parser.add_argument( | |
"--weight_decay", | |
type=float, | |
default=0.0, | |
help="Weight decay to use.", | |
) | |
parser.add_argument( | |
"--num_train_epochs", | |
type=float, | |
default=3.0, | |
help="Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).", | |
) | |
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( | |
"--lr_scheduler_type", | |
type=SchedulerType, | |
default="linear", | |
help="The scheduler type to use.", | |
choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], | |
) | |
parser.add_argument( | |
"--num_warmup_steps", | |
type=int, | |
default=0, | |
help="Number of steps for the warmup in the lr scheduler.", | |
) | |
parser.add_argument( | |
"--checkpointing_steps", | |
type=str, | |
default=None, | |
help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", | |
) | |
parser.add_argument( | |
"--resume_from_checkpoint", | |
type=str, | |
default=None, | |
help="If the training should continue from a checkpoint folder.", | |
) | |
parser.add_argument( | |
"--per_device_eval_batch_size", | |
type=int, | |
default=8, | |
help="Batch size (per device) for the evaluation dataloader.", | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default=None, | |
help="Where to store the final model.", | |
) | |
args = parser.parse_args() | |
# Sanity checks | |
data_files = {} | |
if args.train_dir is not None: | |
data_files["train"] = args.train_dir | |
if args.validation_dir is not None: | |
data_files["val"] = args.validation_dir | |
args.data_files = data_files if data_files else None | |
if args.push_to_hub: | |
assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." | |
return args | |
class MaskGenerator: | |
""" | |
A class to generate boolean masks for the pretraining task. | |
A mask is a 1D tensor of shape (model_patch_size**2,) where the value is either 0 or 1, | |
where 1 indicates "masked". | |
""" | |
def __init__(self, input_size=192, mask_patch_size=32, model_patch_size=4, mask_ratio=0.6): | |
self.input_size = input_size | |
self.mask_patch_size = mask_patch_size | |
self.model_patch_size = model_patch_size | |
self.mask_ratio = mask_ratio | |
if self.input_size % self.mask_patch_size != 0: | |
raise ValueError("Input size must be divisible by mask patch size") | |
if self.mask_patch_size % self.model_patch_size != 0: | |
raise ValueError("Mask patch size must be divisible by model patch size") | |
self.rand_size = self.input_size // self.mask_patch_size | |
self.scale = self.mask_patch_size // self.model_patch_size | |
self.token_count = self.rand_size**2 | |
self.mask_count = int(np.ceil(self.token_count * self.mask_ratio)) | |
def __call__(self): | |
mask_idx = np.random.permutation(self.token_count)[: self.mask_count] | |
mask = np.zeros(self.token_count, dtype=int) | |
mask[mask_idx] = 1 | |
mask = mask.reshape((self.rand_size, self.rand_size)) | |
mask = mask.repeat(self.scale, axis=0).repeat(self.scale, axis=1) | |
return torch.tensor(mask.flatten()) | |
def collate_fn(examples): | |
pixel_values = torch.stack([example["pixel_values"] for example in examples]) | |
mask = torch.stack([example["mask"] for example in examples]) | |
return {"pixel_values": pixel_values, "bool_masked_pos": mask} | |
def main(): | |
args = parse_args() | |
if args.use_auth_token is not None: | |
warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning) | |
if args.token is not None: | |
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") | |
args.token = args.use_auth_token | |
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The | |
# information sent is the one passed as arguments along with your Python/PyTorch versions. | |
send_example_telemetry("run_mim_no_trainer", args) | |
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example. | |
# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers | |
# in the environment | |
accelerator_log_kwargs = {} | |
if args.with_tracking: | |
accelerator_log_kwargs["log_with"] = args.report_to | |
accelerator_log_kwargs["project_dir"] = args.output_dir | |
accelerator = Accelerator( | |
gradient_accumulation_steps=args.gradient_accumulation_steps, | |
**accelerator_log_kwargs, | |
) | |
# 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) | |
if accelerator.is_local_main_process: | |
datasets.utils.logging.set_verbosity_warning() | |
transformers.utils.logging.set_verbosity_info() | |
else: | |
datasets.utils.logging.set_verbosity_error() | |
transformers.utils.logging.set_verbosity_error() | |
# If passed along, set the training seed now. | |
if args.seed is not None: | |
set_seed(args.seed) | |
# Handle the repository creation | |
if accelerator.is_main_process: | |
if args.push_to_hub: | |
# Retrieve of infer repo_name | |
repo_name = args.hub_model_id | |
if repo_name is None: | |
repo_name = Path(args.output_dir).absolute().name | |
# Create repo and retrieve repo_id | |
repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id | |
# Clone repo locally | |
repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token) | |
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: | |
if "step_*" not in gitignore: | |
gitignore.write("step_*\n") | |
if "epoch_*" not in gitignore: | |
gitignore.write("epoch_*\n") | |
elif args.output_dir is not None: | |
os.makedirs(args.output_dir, exist_ok=True) | |
accelerator.wait_for_everyone() | |
# Initialize our dataset. | |
ds = load_dataset( | |
args.dataset_name, | |
args.dataset_config_name, | |
data_files=args.data_files, | |
cache_dir=args.cache_dir, | |
token=args.token, | |
) | |
# If we don't have a validation split, split off a percentage of train as validation. | |
args.train_val_split = None if "validation" in ds.keys() else args.train_val_split | |
if isinstance(args.train_val_split, float) and args.train_val_split > 0.0: | |
split = ds["train"].train_test_split(args.train_val_split) | |
ds["train"] = split["train"] | |
ds["validation"] = split["test"] | |
# Create config | |
# Distributed training: | |
# The .from_pretrained methods guarantee that only one local process can concurrently | |
# download model & vocab. | |
config_kwargs = { | |
"cache_dir": args.cache_dir, | |
"revision": args.model_revision, | |
"token": args.token, | |
"trust_remote_code": args.trust_remote_code, | |
} | |
if args.config_name_or_path: | |
config = AutoConfig.from_pretrained(args.config_name_or_path, **config_kwargs) | |
elif args.model_name_or_path: | |
config = AutoConfig.from_pretrained(args.model_name_or_path, **config_kwargs) | |
else: | |
config = CONFIG_MAPPING[args.model_type]() | |
logger.warning("You are instantiating a new config instance from scratch.") | |
if args.config_overrides is not None: | |
logger.info(f"Overriding config: {args.config_overrides}") | |
config.update_from_string(args.config_overrides) | |
logger.info(f"New config: {config}") | |
# make sure the decoder_type is "simmim" (only relevant for BEiT) | |
if hasattr(config, "decoder_type"): | |
config.decoder_type = "simmim" | |
# adapt config | |
args.image_size = args.image_size if args.image_size is not None else config.image_size | |
args.patch_size = args.patch_size if args.patch_size is not None else config.patch_size | |
args.encoder_stride = args.encoder_stride if args.encoder_stride is not None else config.encoder_stride | |
config.update( | |
{ | |
"image_size": args.image_size, | |
"patch_size": args.patch_size, | |
"encoder_stride": args.encoder_stride, | |
} | |
) | |
# create image processor | |
if args.image_processor_name: | |
image_processor = AutoImageProcessor.from_pretrained(args.image_processor_name, **config_kwargs) | |
elif args.model_name_or_path: | |
image_processor = AutoImageProcessor.from_pretrained(args.model_name_or_path, **config_kwargs) | |
else: | |
IMAGE_PROCESSOR_TYPES = { | |
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items() | |
} | |
image_processor = IMAGE_PROCESSOR_TYPES[args.model_type]() | |
# create model | |
if args.model_name_or_path: | |
model = AutoModelForMaskedImageModeling.from_pretrained( | |
args.model_name_or_path, | |
from_tf=bool(".ckpt" in args.model_name_or_path), | |
config=config, | |
cache_dir=args.cache_dir, | |
revision=args.model_revision, | |
token=args.token, | |
trust_remote_code=args.trust_remote_code, | |
) | |
else: | |
logger.info("Training new model from scratch") | |
model = AutoModelForMaskedImageModeling.from_config( | |
config, | |
token=args.token, | |
trust_remote_code=args.trust_remote_code, | |
) | |
column_names = ds["train"].column_names | |
if args.image_column_name is not None: | |
image_column_name = args.image_column_name | |
elif "image" in column_names: | |
image_column_name = "image" | |
elif "img" in column_names: | |
image_column_name = "img" | |
else: | |
image_column_name = column_names[0] | |
# transformations as done in original SimMIM paper | |
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py | |
transforms = Compose( | |
[ | |
Lambda(lambda img: img.convert("RGB")), | |
RandomResizedCrop(args.image_size, scale=(0.67, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)), | |
RandomHorizontalFlip(), | |
ToTensor(), | |
Normalize(mean=image_processor.image_mean, std=image_processor.image_std), | |
] | |
) | |
# create mask generator | |
mask_generator = MaskGenerator( | |
input_size=args.image_size, | |
mask_patch_size=args.mask_patch_size, | |
model_patch_size=args.patch_size, | |
mask_ratio=args.mask_ratio, | |
) | |
def preprocess_images(examples): | |
"""Preprocess a batch of images by applying transforms + creating a corresponding mask, indicating | |
which patches to mask.""" | |
examples["pixel_values"] = [transforms(image) for image in examples[image_column_name]] | |
examples["mask"] = [mask_generator() for i in range(len(examples[image_column_name]))] | |
return examples | |
if args.max_train_samples is not None: | |
ds["train"] = ds["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) | |
# Set the training transforms | |
ds["train"].set_transform(preprocess_images) | |
if args.max_eval_samples is not None: | |
ds["validation"] = ds["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples)) | |
# Set the validation transforms | |
ds["validation"].set_transform(preprocess_images) | |
# DataLoaders creation: | |
train_dataloader = DataLoader( | |
ds["train"], | |
shuffle=True, | |
collate_fn=collate_fn, | |
batch_size=args.per_device_train_batch_size, | |
) | |
eval_dataloader = DataLoader( | |
ds["validation"], | |
collate_fn=collate_fn, | |
batch_size=args.per_device_eval_batch_size, | |
) | |
# Optimizer | |
# Split weights in two groups, one with weight decay and the other not. | |
no_decay = ["bias", "LayerNorm.weight"] | |
optimizer_grouped_parameters = [ | |
{ | |
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], | |
"weight_decay": args.weight_decay, | |
}, | |
{ | |
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], | |
"weight_decay": 0.0, | |
}, | |
] | |
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) | |
# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be | |
# shorter in multiprocess) | |
# 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( | |
name=args.lr_scheduler_type, | |
optimizer=optimizer, | |
num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, | |
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, | |
) | |
# Prepare everything with our `accelerator`. | |
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( | |
model, | |
optimizer, | |
train_dataloader, | |
eval_dataloader, | |
lr_scheduler, | |
) | |
# On TPU, the tie weights in our model have been disconnected, so we need to restore the ties. | |
if accelerator.distributed_type == DistributedType.TPU: | |
model.tie_weights() | |
# 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) | |
# Figure out how many steps we should save the Accelerator states | |
checkpointing_steps = args.checkpointing_steps | |
if checkpointing_steps is not None and checkpointing_steps.isdigit(): | |
checkpointing_steps = int(checkpointing_steps) | |
# We need to initialize the trackers we use, and also store our configuration. | |
# The trackers initializes automatically on the main process. | |
if args.with_tracking: | |
experiment_config = vars(args) | |
# TensorBoard cannot log Enums, need the raw value | |
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value | |
accelerator.init_trackers("mim_no_trainer", experiment_config) | |
# Train! | |
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps | |
logger.info("***** Running training *****") | |
logger.info(f" Num examples = {len(ds['train'])}") | |
logger.info(f" Num Epochs = {args.num_train_epochs}") | |
logger.info(f" Instantaneous batch size per device = {args.per_device_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}") | |
# Only show the progress bar once on each machine. | |
progress_bar = tqdm(range(int(args.max_train_steps)), disable=not accelerator.is_local_main_process) | |
completed_steps = 0 | |
starting_epoch = 0 | |
# Potentially load in the weights and states from a previous save | |
if args.resume_from_checkpoint: | |
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": | |
checkpoint_path = args.resume_from_checkpoint | |
path = os.path.basename(args.resume_from_checkpoint) | |
else: | |
# Get the most recent checkpoint | |
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] | |
dirs.sort(key=os.path.getctime) | |
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last | |
checkpoint_path = path | |
path = os.path.basename(checkpoint_path) | |
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}") | |
accelerator.load_state(path) | |
# Extract `epoch_{i}` or `step_{i}` | |
training_difference = os.path.splitext(path)[0] | |
if "epoch" in training_difference: | |
starting_epoch = int(training_difference.replace("epoch_", "")) + 1 | |
resume_step = None | |
completed_steps = starting_epoch * num_update_steps_per_epoch | |
else: | |
# need to multiply `gradient_accumulation_steps` to reflect real steps | |
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps | |
starting_epoch = resume_step // len(train_dataloader) | |
completed_steps = resume_step // args.gradient_accumulation_steps | |
resume_step -= starting_epoch * len(train_dataloader) | |
# update the progress_bar if load from checkpoint | |
progress_bar.update(completed_steps) | |
for epoch in range(starting_epoch, args.num_train_epochs): | |
model.train() | |
if args.with_tracking: | |
total_loss = 0 | |
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None: | |
# We skip the first `n` batches in the dataloader when resuming from a checkpoint | |
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step) | |
else: | |
active_dataloader = train_dataloader | |
for step, batch in enumerate(active_dataloader): | |
with accelerator.accumulate(model): | |
outputs = model(**batch) | |
loss = outputs.loss | |
# We keep track of the loss at each epoch | |
if args.with_tracking: | |
total_loss += loss.detach().float() | |
accelerator.backward(loss) | |
optimizer.step() | |
lr_scheduler.step() | |
optimizer.zero_grad() | |
# Checks if the accelerator has performed an optimization step behind the scenes | |
if accelerator.sync_gradients: | |
progress_bar.update(1) | |
completed_steps += 1 | |
if isinstance(checkpointing_steps, int): | |
if completed_steps % checkpointing_steps == 0: | |
output_dir = f"step_{completed_steps}" | |
if args.output_dir is not None: | |
output_dir = os.path.join(args.output_dir, output_dir) | |
accelerator.save_state(output_dir) | |
if completed_steps >= args.max_train_steps: | |
break | |
model.eval() | |
losses = [] | |
for step, batch in enumerate(eval_dataloader): | |
with torch.no_grad(): | |
outputs = model(**batch) | |
loss = outputs.loss | |
losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size))) | |
losses = torch.cat(losses) | |
eval_loss = torch.mean(losses) | |
logger.info(f"epoch {epoch}: eval_loss: {eval_loss}") | |
if args.with_tracking: | |
accelerator.log( | |
{ | |
"eval_loss": eval_loss, | |
"train_loss": total_loss.item() / len(train_dataloader), | |
"epoch": epoch, | |
"step": completed_steps, | |
}, | |
step=completed_steps, | |
) | |
if args.push_to_hub and epoch < args.num_train_epochs - 1: | |
accelerator.wait_for_everyone() | |
unwrapped_model = accelerator.unwrap_model(model) | |
unwrapped_model.save_pretrained( | |
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save | |
) | |
if accelerator.is_main_process: | |
image_processor.save_pretrained(args.output_dir) | |
repo.push_to_hub( | |
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True | |
) | |
if args.checkpointing_steps == "epoch": | |
output_dir = f"epoch_{epoch}" | |
if args.output_dir is not None: | |
output_dir = os.path.join(args.output_dir, output_dir) | |
accelerator.save_state(output_dir) | |
if args.with_tracking: | |
accelerator.end_training() | |
if args.output_dir is not None: | |
accelerator.wait_for_everyone() | |
unwrapped_model = accelerator.unwrap_model(model) | |
unwrapped_model.save_pretrained( | |
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save | |
) | |
if accelerator.is_main_process: | |
image_processor.save_pretrained(args.output_dir) | |
if args.push_to_hub: | |
repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) | |
if __name__ == "__main__": | |
main() | |