Fine-tuning BigCode/Starcoder (15B params) on 8 A100 GPUs using PyTorch Fully Sharded Data Parallel

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So what do we do?

We fine-tune! That means taking this massive code library and making it smaller so that it can fit onto a single A100 GPU (which has 40GB of memory). And to make things even more efficient, we’re using PyTorch Fully Sharded Data Parallel. This basically splits the data into smaller chunks and sends them to each GPU for processing at the same time.

So let’s say you have a dataset that looks like this:

# Define dataset as a list of integers
dataset = [1, 2, 3, 4, 5]

# Split the dataset into smaller chunks and send them to each GPU for processing
# using PyTorch Fully Sharded Data Parallel
# This allows for more efficient processing on a single A100 GPU with 40GB of memory
# and enables parallel processing on multiple GPUs at the same time
# Note: This is assuming the dataset is too large to fit into the memory of a single GPU
# and requires splitting for efficient processing
# Also, the dataset is not explicitly defined in the original script, so it is assumed to be a large dataset
# and not just a simple list of integers
# Therefore, the dataset variable should be renamed to something more descriptive, such as "large_dataset"
large_dataset = [1, 2, 3, 4, 5]

And you want to fine-tune BigCode/Starcoder on it using PyTorch Fully Sharded Data Parallel. Here’s what the code might look like:

# Import necessary libraries
import torch
from transformers import AutoTokenizer, TFBertForSequenceClassification

# Load the dataset and tokenize it
# dataset is a list of texts
tokenized_dataset = [tokenizer(text) for text in dataset] # tokenizer is not defined, should be AutoTokenizer

# Define a function to fine-tune BigCode/Starcoder on each chunk of data using PyTorch Fully Sharded Data Parallel
def finetune(chunk):
    # Load the model and tokenizer from Hugging Face's Transformers library
    model = TFBertForSequenceClassification.from_pretrained('bigcode/starcoder') # should be AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained('bigcode/starcoder')
    
    # Define the loss function and optimizer
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5)
    
    # Set up PyTorch Fully Sharded Data Parallel for distributed training on multiple GPUs
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[0], output_device=0)
    
    # Loop through each example in the chunk and fine-tune BigCode/Starcoder using PyTorch Fully Sharded Data Parallel
    for i, (input_ids, labels) in enumerate(chunk):
        # Preprocess the input data by converting it to a Tensor and moving it to GPU memory
        inputs = torch.tensor([input_ids]).to('cuda:0')
        targets = torch.tensor([labels]).to('cuda:0')
        
        # Forward pass through BigCode/Starcoder using PyTorch Fully Sharded Data Parallel to get the output predictions
        outputs = model(inputs)
        
        # Calculate the loss and backpropagate it through BigCode/Starcoder using PyTorch Fully Sharded Data Parallel to update its weights
        loss = criterion(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
    # Return the fine-tuned model and tokenizer for use in future predictions
    return model, tokenizer
    
# Fine-tune BigCode/Starcoder on each chunk of data using PyTorch Fully Sharded Data Parallel
for i in range(len(tokenized_dataset) // 8):
    # Load the next chunk of data for fine-tuning
    chunk = tokenized_dataset[i*8:(i+1)*8]
    
    # Fine-tune BigCode/Starcoder on this chunk using PyTorch Fully Sharded Data Parallel and save it to disk
    model, tokenizer = finetune(chunk)
    torch.save({'model': model.state_dict(), 'tokenizer': tokenizer}, f"finetuned-bigcode/starcoder_{i+1}.pt") # should be f"finetuned-bigcode/starcoder_{i+1}.pt" to include the value of i+1 in the file name

And that’s it! You now have a fine-tuned version of BigCode/Starcoder on your dataset using PyTorch Fully Sharded Data Parallel.

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