Tip

This tutorial is available as a Jupyter notebook.

📖 NLP Models

This tutorial will demonstrate how to fine-tune a pretrained HuggingFace transformer using the composer library! Composer provides a highly optimized and functional training loop and the ability to compose several methods that can accelerate training.

We will focus on fine-tuning a pretrained BERT-base model on the Stanford Sentiment Treebank v2 (SST-2) dataset. After fine-tuning, the BERT model should be able to determine if a setence has positive or negative sentiment.

Let’s do this 🚀

Install Composer

To develop NLP models with Composer, we’ll need to install Composer with the NLP dependencies. If you haven’t already, run:

%pip install 'mosaicml[nlp]'

To log our results using Tensorboard, we will also need to install Tensorboard. If you haven’t already, run:

%pip install 'mosaicml[tensorboard]'

Defining a Composer Model

The first task is to create a composer model. A composer model defines four components for the Composer trainer: - forward() - parses the dataloader output for the model’s forward function and extracts the necessary components of the model’s output for loss calculation. - loss() - computes the loss for the current batch using the model and dataloader outputs. - validate() - parses the dataloader and model output for torchmetrics. - metrics() - defines the torchmetrics to use during training/validation.

import transformers
from torchmetrics import Accuracy
from torchmetrics.collections import MetricCollection
from composer.models.base import ComposerModel
from composer.metrics import CrossEntropy

# Define a Composer Model
class ComposerBERT(ComposerModel):
    def __init__(self, model):
        super().__init__()
        self.module = model

        # Metrics
        self.train_loss = CrossEntropy()
        self.val_loss = CrossEntropy()

        self.train_acc = Accuracy()
        self.val_acc = Accuracy()

    def forward(self, batch):
        output = self.module(**batch)
        return output

    def loss(self, outputs, batch):
        return outputs['loss']

    def validate(self, batch):
        labels = batch.pop('labels')
        output = self.forward(batch)
        output = output['logits']
        return (output, labels)

    def metrics(self, train: bool = False):
        return MetricCollection([self.train_loss, self.train_acc]) \
     if train else MetricCollection([self.val_loss, self.val_acc])

# Create a BERT sequence classification model using HuggingFace transformsers
model = transformers.AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) # in BERT hparams

# Package as a composer model
composer_model = ComposerBERT(model)

Creating dataloaders

Next, we will download and tokenize the SST-2 datasets.

import datasets
from multiprocessing import cpu_count

# Create BERT tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-uncased') # from transformer_shared
def tokenize_function(sample):
    return tokenizer(
        text=sample['sentence'],
        padding="max_length",
        max_length=256,
        truncation=True
    )

# Tokenize SST-2
sst2_dataset = datasets.load_dataset("glue", "sst2")
tokenized_sst2_dataset = sst2_dataset.map(tokenize_function,
                                          batched=True,
                                          num_proc=cpu_count(),
                                          batch_size=1000,
                                          remove_columns=['idx', 'sentence'])

# Split dataset into train and validation sets
train_dataset = tokenized_sst2_dataset["train"]
eval_dataset = tokenized_sst2_dataset["validation"]

Here, we will create a PyTorch DataLoader for each of the datasets generated in the previous block.

from torch.utils.data import DataLoader
data_collator = transformers.data.data_collator.default_data_collator
train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=False, drop_last=False, collate_fn=data_collator)
eval_dataloader = DataLoader(eval_dataset,batch_size=16, shuffle=False, drop_last=False, collate_fn=data_collator)

To use the composer Trainer, we need to define a split_batch function. This function defines how to split the dataloader output into several “microbatches”. Microbatchs are chunks of the batch that were divided based on the amount of gradient accumulation used.

from composer.core import DataSpec

def split_batch_dict(batch, n_microbatches: int):
    chunked = {k: v.chunk(n_microbatches) for k, v in batch.items()}
    num_chunks = len(list(chunked.values())[0])
    return [{k: v[idx] for k, v in chunked.items()} for idx in range(num_chunks)]

train_dataspec = DataSpec(dataloader=train_dataloader,
                          split_batch=split_batch_dict)
eval_dataspec = DataSpec(dataloader=eval_dataloader,
                         split_batch=split_batch_dict)

Optimizers and Learning Rate Schedulers

The last setup step is to create an optimizer and a learning rate scheduler. We will use PyTorch’s AdamW optimizer and linear learning rate scheduler since these are typically used to fine-tune BERT on tasks such as SST-2.

from torch.optim import AdamW
from torch.optim.lr_scheduler import LinearLR

optimizer = AdamW(
    params=composer_model.parameters(),
    lr=3e-5, betas=(0.9, 0.98),
    eps=1e-6, weight_decay=3e-6
)
linear_lr_decay = LinearLR(
    optimizer, start_factor=1.0,
    end_factor=0, total_iters=150
)

Logging to Tensorboard

The last step is to set up the ability to log our metrics to Tensorboard. Tensorboard is nice tool that allows you to track and visualize metrics. To do this we need to create a TensorboardLogger object.

from composer.loggers import TensorboardLogger

# Here we specify that we want our tensorboard logs to be saved to ./tensorboard_logs.
tb_logger = TensorboardLogger(log_dir="./tensorboard_logs")

Composer Trainer

We will now specify a Composer Trainer object and run our training! Trainer has many arguments that are described in our documentation, but let’s discuss the less obvious arguments used below: - max_duration - a string specifying how long to train, either in terms of batches (e.g. ‘10ba’ is 10 batches) or epochs (e.g. ‘1ep’ is 1 epoch). - schedulers - a list of PyTorch learning rate schedulers that will be composed together. - device - specifies if the training will be done on CPU or GPU by using ‘cpu’ or ‘gpu’, respectively. - train_subset_num_batches - specifies the number of training batches to use for each epoch. This is not a necessary argument but is useful for quickly testing code. - precision - whether to do the training in full precision ‘fp32’ or mixed precision ‘amp’. Mixed precision provides an almost 2x speedup in training time on certain hardware. If you get a P100, try precision='amp'! - seed - sets the random seed for the training run, so the results are reproducible! - loggers - sets all the loggers we want to use during our training.

import torch
from composer import Trainer

# Create Trainer Object
trainer = Trainer(
    model=composer_model,
    train_dataloader=train_dataspec,
    eval_dataloader=eval_dataspec,
    max_duration="1ep",
    optimizers=optimizer,
    schedulers=[linear_lr_decay],
    device='gpu' if torch.cuda.is_available() else 'cpu',
    train_subset_num_batches=150,
    precision='fp32',
    seed=17,
    loggers=[tb_logger],
)
# Start training
trainer.fit()

Visualizing Results

Our model reaches almost 86% accuracy with only 100 iterations of training! Let’s visualize a few samples from the validation set to see how our model performs.

eval_batch = next(iter(eval_dataloader))

# Move batch to gpu
eval_batch = {k: v.cuda() if torch.cuda.is_available() else v for k, v in eval_batch.items()}
with torch.no_grad():
    predictions = composer_model(eval_batch)["logits"].argmax(dim=1)

# Visualize only 5 samples
predictions = predictions[:6]

label = ['negative', 'positive']
for i, prediction in enumerate(predictions[:6]):
    sentence = sst2_dataset["validation"][i]["sentence"]
    correct_label = label[sst2_dataset["validation"][i]["label"]]
    prediction_label = label[prediction]
    print(f"Sample: {sentence}")
    print(f"Label: {correct_label}")
    print(f"Prediction: {prediction_label}")
    print()

Conclusion

This tutorial showed how to use the Composer Trainer to fine-tune a pre-trained BERT on a subset of the SST-2 dataset. We focused on the Composer’s basic functionality, but there are many more tools such as easy to use gradient accumulation and multi-GPU training! Check out many other features at our documentation.