#!/usr/bin/env python # 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 # /// script # dependencies = [ # "transformers @ git+https://github.com/huggingface/transformers.git", # "torch>=1.5.0", # "torchvision>=0.6.0", # "datasets>=1.8.0", # ] # /// import argparse import logging import math import os 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 HfApi 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 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("4.57.0.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 `hf auth login` (stored in `~/.huggingface`)." ), ) parser.add_argument( "--trust_remote_code", action="store_true", help=( "Whether to trust the execution of code from datasets/models defined on the Hub." " 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() # 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 api = HfApi() repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id 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, trust_remote_code=args.trust_remote_code, ) # 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 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 * accelerator.num_processes, num_training_steps=args.max_train_steps if overrode_max_train_steps else args.max_train_steps * accelerator.num_processes, ) # 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(checkpoint_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 and accelerator.sync_gradients: 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) api.upload_folder( commit_message=f"Training in progress epoch {epoch}", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) 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.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: api.upload_folder( commit_message="End of training", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) accelerator.wait_for_everyone() accelerator.end_training() if __name__ == "__main__": main()