testbed/huggingface__accelerate/examples/by_feature/cross_validation.py

# coding=utf-8
# Copyright 2022 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.
import argparse
from typing import List

import numpy as np
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader

import evaluate
from accelerate import Accelerator, DistributedType
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


MAX_GPU_BATCH_SIZE = 16
EVAL_BATCH_SIZE = 32

# New Code #
# We need a different `get_dataloaders` function that will build dataloaders by index


def get_fold_dataloaders(
    accelerator: Accelerator, dataset: DatasetDict, train_idxs: List[int], valid_idxs: List[int], batch_size: int = 16
):
    """
    Gets a set of train, valid, and test dataloaders for a particular fold

    Args:
        accelerator (`Accelerator`):
            The main `Accelerator` object
        train_idxs (list of `int`):
            The split indices for the training dataset
        valid_idxs (list of `int`):
            The split indices for the validation dataset
        batch_size (`int`):
            The size of the minibatch. Default is 16
    """
    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
    datasets = DatasetDict(
        {
            "train": dataset["train"].select(train_idxs),
            "validation": dataset["train"].select(valid_idxs),
            "test": dataset["validation"],
        }
    )

    def tokenize_function(examples):
        # max_length=None => use the model max length (it's actually the default)
        outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        tokenized_datasets = datasets.map(
            tokenize_function,
            batched=True,
            remove_columns=["idx", "sentence1", "sentence2"],
        )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")

    def collate_fn(examples):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt")
        return tokenizer.pad(examples, padding="longest", return_tensors="pt")

    # Instantiate dataloaders.
    train_dataloader = DataLoader(
        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
    )
    eval_dataloader = DataLoader(
        tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
    )

    test_dataloader = DataLoader(
        tokenized_datasets["test"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
    )

    return train_dataloader, eval_dataloader, test_dataloader


def training_function(config, args):
    # New Code #
    test_predictions = []
    # Download the dataset
    datasets = load_dataset("glue", "mrpc")
    # Create our splits
    kfold = StratifiedKFold(n_splits=int(args.num_folds))
    # Initialize accelerator
    accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lr = config["lr"]
    num_epochs = int(config["num_epochs"])
    seed = int(config["seed"])
    batch_size = int(config["batch_size"])

    metric = evaluate.load("glue", "mrpc")

    # If the batch size is too big we use gradient accumulation
    gradient_accumulation_steps = 1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
        batch_size = MAX_GPU_BATCH_SIZE

    set_seed(seed)

    # New Code #
    # Create our folds:
    folds = kfold.split(np.zeros(datasets["train"].num_rows), datasets["train"]["label"])
    test_references = []
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(folds):
        train_dataloader, eval_dataloader, test_dataloader = get_fold_dataloaders(
            accelerator,
            datasets,
            train_idxs,
            valid_idxs,
        )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        model = model.to(accelerator.device)

        # Instantiate optimizer
        optimizer = AdamW(params=model.parameters(), lr=lr)

        # Instantiate scheduler
        lr_scheduler = get_linear_schedule_with_warmup(
            optimizer=optimizer,
            num_warmup_steps=100,
            num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
        )

        # Prepare everything
        # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
        # prepare method.
        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
            model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
        )

        # Now we train the model
        for epoch in range(num_epochs):
            model.train()
            for step, batch in enumerate(train_dataloader):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device)
                outputs = model(**batch)
                loss = outputs.loss
                loss = loss / gradient_accumulation_steps
                accelerator.backward(loss)
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(eval_dataloader):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device)
                with torch.no_grad():
                    outputs = model(**batch)
                predictions = outputs.logits.argmax(dim=-1)
                predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
                metric.add_batch(
                    predictions=predictions,
                    references=references,
                )

            eval_metric = metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:", eval_metric)

        # New Code #
        # We also run predictions on the test set at the very end
        fold_predictions = []
        for step, batch in enumerate(test_dataloader):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device)
            with torch.no_grad():
                outputs = model(**batch)
            predictions = outputs.logits
            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
            fold_predictions.append(predictions.cpu())
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu())
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(fold_predictions, dim=0))
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    test_references = torch.cat(test_references, dim=0)
    preds = torch.stack(test_predictions, dim=0).sum(dim=0).div(int(args.num_folds)).argmax(dim=-1)
    test_metric = metric.compute(predictions=preds, references=test_references)
    accelerator.print("Average test metrics from all folds:", test_metric)


def main():
    parser = argparse.ArgumentParser(description="Simple example of training script.")
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="no",
        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.",
    )
    parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
    # New Code #
    parser.add_argument("--num_folds", type=int, default=3, help="The number of splits to perform across the dataset")
    args = parser.parse_args()
    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(config, args)


if __name__ == "__main__":
    main()