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https://github.com/huggingface/accelerate.git
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726140cad2
* v1 * More testing, need to try on H100 * Bigger batch for h100 test * test tweak * Fixup all tests! * Bookmark * Fix issues, working now * rm num samples * Uncomment * Give stateful dl end of dl * Make skip DL stateful * Migrate to update_state_dict * try/finally * Add comments to test * rm comment * Document * refactor out for eventual override * Doc nit * Brute force it
330 lines
14 KiB
Python
330 lines
14 KiB
Python
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import re
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import numpy as np
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import PIL
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import torch
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from timm import create_model
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from torch.optim.lr_scheduler import OneCycleLR
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from torch.utils.data import DataLoader, Dataset
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from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
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from accelerate import Accelerator, DataLoaderConfiguration
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########################################################################
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# This is a fully working simple example to use Accelerate
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#
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# This example trains a ResNet50 on the Oxford-IIT Pet Dataset
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# in any of the following settings (with the same script):
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# - single CPU or single GPU
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# - multi GPUS (using PyTorch distributed mode)
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# - (multi) TPUs
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# - fp16 (mixed-precision) or fp32 (normal precision)
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#
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# To run it in each of these various modes, follow the instructions
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# in the readme for examples:
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# https://github.com/huggingface/accelerate/tree/main/examples
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#
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########################################################################
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# Function to get the label from the filename
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def extract_label(fname):
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stem = fname.split(os.path.sep)[-1]
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return re.search(r"^(.*)_\d+\.jpg$", stem).groups()[0]
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class PetsDataset(Dataset):
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def __init__(self, file_names, image_transform=None, label_to_id=None):
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self.file_names = file_names
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self.image_transform = image_transform
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self.label_to_id = label_to_id
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def __len__(self):
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return len(self.file_names)
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def __getitem__(self, idx):
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fname = self.file_names[idx]
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raw_image = PIL.Image.open(fname)
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image = raw_image.convert("RGB")
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if self.image_transform is not None:
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image = self.image_transform(image)
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label = extract_label(fname)
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if self.label_to_id is not None:
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label = self.label_to_id[label]
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return {"image": image, "label": label}
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def training_function(config, args):
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# Initialize accelerator
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dataloader_config = DataLoaderConfiguration(use_stateful_dataloader=args.use_stateful_dataloader)
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if args.with_tracking:
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accelerator = Accelerator(
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cpu=args.cpu,
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mixed_precision=args.mixed_precision,
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log_with="all",
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project_dir=args.project_dir,
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dataloader_config=dataloader_config,
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)
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else:
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accelerator = Accelerator(
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cpu=args.cpu, mixed_precision=args.mixed_precision, dataloader_config=dataloader_config
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)
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# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
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lr = config["lr"]
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num_epochs = int(config["num_epochs"])
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seed = int(config["seed"])
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batch_size = int(config["batch_size"])
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image_size = config["image_size"]
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if not isinstance(image_size, (list, tuple)):
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image_size = (image_size, image_size)
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# Parse out whether we are saving every epoch or after a certain number of batches
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if hasattr(args.checkpointing_steps, "isdigit"):
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if args.checkpointing_steps == "epoch":
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checkpointing_steps = args.checkpointing_steps
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elif args.checkpointing_steps.isdigit():
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checkpointing_steps = int(args.checkpointing_steps)
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else:
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raise ValueError(
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f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."
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)
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else:
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checkpointing_steps = None
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# We need to initialize the trackers we use, and also store our configuration
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if args.with_tracking:
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run = os.path.split(__file__)[-1].split(".")[0]
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accelerator.init_trackers(run, config)
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# Grab all the image filenames
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file_names = [os.path.join(args.data_dir, fname) for fname in os.listdir(args.data_dir) if fname.endswith(".jpg")]
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# Build the label correspondences
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all_labels = [extract_label(fname) for fname in file_names]
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id_to_label = list(set(all_labels))
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id_to_label.sort()
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label_to_id = {lbl: i for i, lbl in enumerate(id_to_label)}
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# Set the seed before splitting the data.
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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# Split our filenames between train and validation
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random_perm = np.random.permutation(len(file_names))
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cut = int(0.8 * len(file_names))
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train_split = random_perm[:cut]
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eval_split = random_perm[cut:]
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# For training we use a simple RandomResizedCrop
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train_tfm = Compose([RandomResizedCrop(image_size, scale=(0.5, 1.0)), ToTensor()])
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train_dataset = PetsDataset(
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[file_names[i] for i in train_split], image_transform=train_tfm, label_to_id=label_to_id
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)
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# For evaluation, we use a deterministic Resize
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eval_tfm = Compose([Resize(image_size), ToTensor()])
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eval_dataset = PetsDataset([file_names[i] for i in eval_split], image_transform=eval_tfm, label_to_id=label_to_id)
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# Instantiate dataloaders.
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train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size, num_workers=4)
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eval_dataloader = DataLoader(eval_dataset, shuffle=False, batch_size=batch_size, num_workers=4)
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# Instantiate the model (we build the model here so that the seed also control new weights initialization)
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model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id))
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# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
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# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
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# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
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model = model.to(accelerator.device)
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# Freezing the base model
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for param in model.parameters():
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param.requires_grad = False
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for param in model.get_classifier().parameters():
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param.requires_grad = True
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# We normalize the batches of images to be a bit faster.
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mean = torch.tensor(model.default_cfg["mean"])[None, :, None, None].to(accelerator.device)
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std = torch.tensor(model.default_cfg["std"])[None, :, None, None].to(accelerator.device)
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# Instantiate optimizer
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optimizer = torch.optim.Adam(params=model.parameters(), lr=lr / 25)
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# Instantiate learning rate scheduler
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lr_scheduler = OneCycleLR(optimizer=optimizer, max_lr=lr, epochs=num_epochs, steps_per_epoch=len(train_dataloader))
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# Prepare everything
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# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
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# prepare method.
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model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
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)
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# We need to keep track of how many total steps we have iterated over
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overall_step = 0
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# We also need to keep track of the starting epoch so files are named properly
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starting_epoch = 0
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# Potentially load in the weights and states from a previous save
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if args.resume_from_checkpoint:
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if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
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accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
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accelerator.load_state(args.resume_from_checkpoint)
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path = os.path.basename(args.resume_from_checkpoint)
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else:
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# Get the most recent checkpoint
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dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
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dirs.sort(key=os.path.getctime)
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path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
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# Extract `epoch_{i}` or `step_{i}`
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training_difference = os.path.splitext(path)[0]
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if "epoch" in training_difference:
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starting_epoch = int(training_difference.replace("epoch_", "")) + 1
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resume_step = None
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else:
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resume_step = int(training_difference.replace("step_", ""))
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starting_epoch = resume_step // len(train_dataloader)
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resume_step -= starting_epoch * len(train_dataloader)
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# Now we train the model
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for epoch in range(starting_epoch, num_epochs):
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model.train()
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if args.with_tracking:
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total_loss = 0
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if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
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# We need to skip steps until we reach the resumed step
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active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
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overall_step += resume_step
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else:
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# After the first iteration though, we need to go back to the original dataloader
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active_dataloader = train_dataloader
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for batch in active_dataloader:
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# We could avoid this line since we set the accelerator with `device_placement=True`.
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batch = {k: v.to(accelerator.device) for k, v in batch.items()}
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inputs = (batch["image"] - mean) / std
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outputs = model(inputs)
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loss = torch.nn.functional.cross_entropy(outputs, batch["label"])
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# We keep track of the loss at each epoch
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if args.with_tracking:
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total_loss += loss.detach().float()
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accelerator.backward(loss)
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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overall_step += 1
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if isinstance(checkpointing_steps, int):
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output_dir = f"step_{overall_step}"
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if overall_step % checkpointing_steps == 0:
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if args.output_dir is not None:
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output_dir = os.path.join(args.output_dir, output_dir)
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accelerator.save_state(output_dir)
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model.eval()
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accurate = 0
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num_elems = 0
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for step, batch in enumerate(eval_dataloader):
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# We could avoid this line since we set the accelerator with `device_placement=True`.
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batch = {k: v.to(accelerator.device) for k, v in batch.items()}
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inputs = (batch["image"] - mean) / std
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with torch.no_grad():
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outputs = model(inputs)
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predictions = outputs.argmax(dim=-1)
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predictions, references = accelerator.gather_for_metrics((predictions, batch["label"]))
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accurate_preds = predictions == references
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num_elems += accurate_preds.shape[0]
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accurate += accurate_preds.long().sum()
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eval_metric = accurate.item() / num_elems
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# Use accelerator.print to print only on the main process.
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accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}")
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if args.with_tracking:
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accelerator.log(
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{
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"accuracy": 100 * eval_metric,
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"train_loss": total_loss.item() / len(train_dataloader),
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"epoch": epoch,
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},
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step=overall_step,
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)
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if checkpointing_steps == "epoch":
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output_dir = f"epoch_{epoch}"
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if args.output_dir is not None:
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output_dir = os.path.join(args.output_dir, output_dir)
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accelerator.save_state(output_dir)
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accelerator.end_training()
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def main():
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parser = argparse.ArgumentParser(description="Simple example of training script.")
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parser.add_argument("--data_dir", required=True, help="The data folder on disk.")
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parser.add_argument("--fp16", action="store_true", help="If passed, will use FP16 training.")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16", "fp8"],
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help="Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU.",
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)
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parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
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parser.add_argument(
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"--checkpointing_steps",
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type=str,
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default=None,
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help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default=".",
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help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help="If the training should continue from a checkpoint folder.",
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)
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parser.add_argument(
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"--use_stateful_dataloader",
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action="store_true",
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help="If the dataloader should be a resumable stateful dataloader.",
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)
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parser.add_argument(
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"--with_tracking",
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action="store_true",
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help="Whether to load in all available experiment trackers from the environment and use them for logging.",
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)
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parser.add_argument(
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"--project_dir",
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type=str,
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default="logs",
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help="Location on where to store experiment tracking logs` and relevent project information",
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)
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args = parser.parse_args()
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config = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
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training_function(config, args)
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if __name__ == "__main__":
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main()
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