These GPUs have a feature called “Flash Attention 2” which allows us to speed up the training process by doing some clever math tricks. And then, when we hit a snag in our calculations (which can happen sometimes), we use another technique called “Gradient Checkpointing” to help us figure out where we went wrong and fix it.
Here’s an example of how this might look in code:
# Load the necessary libraries and data
import torch
from transformers import AutoTokenizer, TFBertForSequenceClassification
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import tensorflow as tf
# Define some hyperparameters for our model (e.g., number of epochs to run)
num_epochs = 10 # Number of times the model will go through the entire dataset during training
batch_size = 32 # Number of data points used in each iteration of training
learning_rate = 5e-5 # Controls how much the model's parameters are updated during training
# Load the data and preprocess it as needed
data = pd.read_csv('my_dataset.csv') # Load the dataset from a CSV file
X, y = data['text'], data['label'] # Separate the input data (text) and labels (target variable)
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Load the tokenizer from the BERT model
max_len = 128 # Set the maximum length of our input sequences (in this case, we're using BERT)
X = tokenizer(X, padding=True, truncation=True, max_length=max_len).input_ids.tolist() # Tokenize the input data and convert it to a list of input IDs
y = np.array([int(i) for i in y]) # Convert the labels to integers (e.g., 0 or 1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Split the data into training and testing sets
# Define our model and compile it for training
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased') # Load the pretrained BERT model from Hugging Face's repository
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Compile our model using Adam optimizer and sparse categorical cross entropy loss function (which is commonly used for text classification tasks)
# Train the model on our data using Flash Attention 2 and Gradient Checkpointing
model.fit(X_train, y_train, batch_size=batch_size, epochs=num_epochs, validation_data=(X_test, y_test), callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1)]) # Train our model using the fit() function from Keras (which is part of TensorFlow). We're passing in some arguments to control how many epochs we run, what size batches we use, and whether or not we want to stop training early if the validation loss doesn't improve after a certain number of iterations.
In this example, we’re using TensorFlow Keras (which is part of Google’s TensorFlow library) to train our model on some text classification data. We first load in our dataset and preprocess it as needed (e.g., tokenizing the input sequences). Then, we define some hyperparameters for our model (such as the number of epochs to run), compile it using Adam optimizer and sparse categorical cross entropy loss function, and train it on our data using Flash Attention 2 and Gradient Checkpointing.
Flash Attention 2 is a technique that allows us to speed up the training process by doing some clever math tricks (such as fusing multiple attention heads together). And when we hit a snag in our calculations, Gradient Checkpointing helps us figure out where we went wrong and fix it. This can be especially useful for large language models with millions of parameters that would otherwise take forever to train on traditional GPUs.
Overall, using Flash Attention 2 and Gradient Checkpointing can help us improve the efficiency and accuracy of our model training process, which is essential for building state-of-the-art natural language processing systems.