testbed/huggingface__accelerate/examples/by_feature/automatic_gradient_accumulation.py

# Copyright 2022 The HuggingFace 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
import os

import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader

# New Code #
import evaluate
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
from datasets import load_dataset
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 combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

EVAL_BATCH_SIZE = 32


def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
    """
    Creates a set of `DataLoader`s for the `glue` dataset,
    using "bert-base-cased" as the tokenizer.

    Args:
        accelerator (`Accelerator`):
            An `Accelerator` object
        batch_size (`int`, *optional*):
            The batch size for the train and validation DataLoaders.
    """
    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
    datasets = load_dataset("glue", "mrpc")

    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
    )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    get_dataloaders = mocked_dataloaders  # noqa: F811


def training_function(config, args):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
        config["num_epochs"] = 2
    # 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"])
    observed_batch_size = int(config["batch_size"])

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

    # New Code #
    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(observed_batch_size))
    def inner_training_loop(batch_size):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        num_gradient_accumulation_steps = observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        accelerator.gradient_accumulation_steps = num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        set_seed(seed)

        # 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)
        train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)

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

        # 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):
                # And perform gradient accumulation
                with accelerator.accumulate(model):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device)
                    outputs = model(**batch)
                    loss = outputs.loss
                    accelerator.backward(loss)
                    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 #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()


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.")
    args = parser.parse_args()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
    training_function(config, args)


if __name__ == "__main__":
    main()