Why would you want to do this? Well, it’s cheaper and faster than training on regular GPUs! And since we’re pre-training the model instead of fine-tuning it for specific tasks like sentiment analysis or question answering, we can use it as a starting point for other language processing projects.
So how does it work exactly? First, we need to prepare our dataset by cleaning and tokenizing text data (turning words into numbers that computers can understand). Then, we preprocess the data using techniques like lowercasing, punctuation removal, and stopword filtering. Finally, we train the model on Habana Gaudi using a special wrapper called GaudiTrainer instead of Trainer because it’s optimized for this specific chip architecture.
Here’s an example script that shows how to do all these steps:
# Import necessary libraries and datasets
import os
from transformers import AutoTokenizer, BertForMaskedLM, GaudiTrainingArguments, GaudiTrainer
from optimum.habana_utils import get_dl1_instance
# Set up the DL1 instance using Optimum Habana library
os.environ['HABANA_DEVICE'] = '0' # Set the device ID for Habana Gaudi
device, _ = get_dl1_instance() # Get the DL1 instance and its first GPU
# Load pre-trained BERT model from Hugging Face Hub
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
# Prepare tokenizer for text data
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) # Load the pre-trained BERT tokenizer from Hugging Face Hub
# Define training arguments and dataset
training_args = GaudiTrainingArguments(output_dir='./outputs/', num_train_epochs=3, per_device_train_batch_size=16) # Set up the training arguments for Habana Gaudi (e.g., output directory, number of epochs, batch size)
dataset = load_and_cache_examples('path/to/your/data', tokenizer) # Load and cache your data using a custom function or library like Datasets
# Preprocess the dataset for training
train_datasets = dataset.map(preprocess_function, batched=True) # Apply preprocessing functions to each batch of data (e.g., lowercasing, punctuation removal, stopword filtering)
# Train the model on Habana Gaudi using GaudiTrainer wrapper
trainer = GaudiTrainer(model=model, args=training_args, train_dataset=train_datasets) # Create a new instance of GaudiTrainer with your pre-trained BERT model and training arguments
trainer.train() # Run the training process on Habana Gaudi using the GaudiTrainer wrapper
# Import necessary libraries and datasets
import os # Import the os library to access operating system functionalities
from transformers import AutoTokenizer, BertForMaskedLM, GaudiTrainingArguments, GaudiTrainer # Import necessary libraries from the transformers package
from optimum.habana_utils import get_dl1_instance # Import the get_dl1_instance function from the optimum.habana_utils library
# Set up the DL1 instance using Optimum Habana library
os.environ['HABANA_DEVICE'] = '0' # Set the device ID for Habana Gaudi
device, _ = get_dl1_instance() # Get the DL1 instance and its first GPU
# Load pre-trained BERT model from Hugging Face Hub
model = BertForMaskedLM.from_pretrained('bert-base-uncased') # Load the pre-trained BERT model from the Hugging Face Hub
# Prepare tokenizer for text data
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) # Load the pre-trained BERT tokenizer from Hugging Face Hub and set the do_lower_case parameter to True to lowercase all text
# Define training arguments and dataset
training_args = GaudiTrainingArguments(output_dir='./outputs/', num_train_epochs=3, per_device_train_batch_size=16) # Set up the training arguments for Habana Gaudi, including the output directory, number of epochs, and batch size
dataset = load_and_cache_examples('path/to/your/data', tokenizer) # Load and cache your data using a custom function or library like Datasets
# Preprocess the dataset for training
train_datasets = dataset.map(preprocess_function, batched=True) # Apply preprocessing functions to each batch of data, such as lowercasing, punctuation removal, and stopword filtering
# Train the model on Habana Gaudi using GaudiTrainer wrapper
trainer = GaudiTrainer(model=model, args=training_args, train_dataset=train_datasets) # Create a new instance of GaudiTrainer with your pre-trained BERT model and training arguments
trainer.train() # Run the training process on Habana Gaudi using the GaudiTrainer wrapper
And that’s it! You now have a pre-trained language model that you can use for other tasks or fine-tune for specific applications like sentiment analysis, question answering, and more.