Fine-Tuning a Code Generation Model with Amazon Bedrock

AI-powered code generation tools like GitHub Copilot and Amazon CodeWhisperer have become indispensable for many developers, but they have a limitation: they are trained on vast, public datasets. While they are great at general-purpose coding, they lack knowledge of your organization's private codebases, internal libraries, and unique architectural patterns.

What if you could create a code generation model that thinks like a senior developer on your team? This is where fine-tuning comes in. Amazon Bedrock, AWS's service for building with foundation models (FMs), allows you to take a base model and fine-tune it on your own data, creating a customized model that is an expert in your code.

Why Fine-Tune a Model for Code Generation?

• Domain-Specific Knowledge: A fine-tuned model understands your internal frameworks, naming conventions, and proprietary APIs. It can generate code that is not just syntactically correct, but also idiomatically correct for your team.
• Improved Accuracy: By training on your existing high-quality code, the model learns your patterns and is more likely to generate accurate, bug-free suggestions.
• Accelerated Onboarding: A fine-tuned model can act as a virtual mentor for new developers, guiding them to use your internal libraries and patterns correctly from day one.
• Code Modernization: You can fine-tune a model on examples of legacy code being refactored into a modern pattern. The model can then assist in automating large-scale code modernization efforts.

The Fine-Tuning Process in Amazon Bedrock

Fine-tuning a model in Bedrock involves a few key steps:

Step 1: Prepare Your Training Data

This is the most critical step. The quality of your fine-tuned model is directly proportional to the quality of your training data. For code generation, your training data should be a set of high-quality prompt-completion pairs in JSON Lines format.

Each line in your training file should be a JSON object with a prompt and a completion:

{"prompt": "// A Python function to get a user from DynamoDB using boto3-assist", "completion": "def get_user_by_id(user_id: str) -> Optional[User]:\n    user_to_find = User(id=user_id)\n    response = db.get(model=user_to_find, table_name='MyTable')\n    return User().map(response.get('Item'))"}
{"prompt": "// A CDK construct for a secure S3 bucket", "completion": "class SecureBucket(Construct):\n    def __init__(self, scope: Construct, id: str):\n        super().__init__(scope, id)\n        self.bucket = s3.Bucket(self, 'Bucket', block_public_access=s3.BlockPublicAccess.BLOCK_ALL, encryption=s3.BucketEncryption.S3_MANAGED)"}

• The Prompt: This is the input you would give to the model, such as a comment or a function signature.
• The Completion: This is the ideal code that the model should generate in response to the prompt.

To build this dataset, you can write scripts to parse your existing codebase, extracting function definitions, class implementations, and their corresponding docstrings or comments.

Step 2: Upload Your Data to S3

Once you have your training dataset (and optionally, a validation dataset), upload it to an S3 bucket.

Step 3: Create a Fine-Tuning Job in Bedrock

In the Amazon Bedrock console:

1. Navigate to the "Fine-tuning" section.
2. Choose a base model to fine-tune. For code generation, a model like Amazon Titan CodeGen or Cohere Command is a good starting point.
3. Configure the fine-tuning job:
   • Provide the S3 paths to your training and validation data.
   • Set hyperparameters like the number of epochs, batch size, and learning rate. Bedrock provides sensible defaults.
   • Specify an output S3 location for the resulting model.
4. Start the job. Fine-tuning can take several hours, depending on the size of your dataset and the model.

Step 4: Use Your Custom Model

Once the job is complete, you will have a new, private, fine-tuned model available in your Bedrock account. You can now invoke this model via the Bedrock API just like you would any other foundation model. You can integrate it into your IDE, build internal CLI tools with it, or use it in your CI/CD pipelines to automate code reviews or generate documentation.

import boto3
import json

bedrock_runtime = boto3.client('bedrock-runtime')

def generate_code(prompt: str):
    response = bedrock_runtime.invoke_model(
        modelId='arn:aws:bedrock:us-east-1:123456789012:provisioned-model/your-custom-model-id',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            "inputText": prompt,
            "textGenerationConfig": {
                "maxTokenCount": 512,
                "temperature": 0.7
            }
        })
    )
    
    response_body = json.loads(response['body'].read())
    return response_body['results'][0]['outputText']

Conclusion

Fine-tuning is the next frontier in AI-assisted development. While general-purpose models are powerful, a model that has been trained on your own high-quality code and internal patterns is a significant force multiplier.

With Amazon Bedrock, the ability to create these custom, expert models is now accessible to all developers. By investing in a high-quality training dataset, you can build a code generation assistant that not only writes code faster but also writes your code better, enforcing best practices and accelerating your entire development lifecycle.