Introducing Morph

Code transformation should be fast, reliable, and accurate. Whether you're adding type hints, converting between languages, or implementing new features, manually updating code is time-consuming and error-prone.

What is Morph?

Morph is a specialized Large Language Model (LLM) service designed specifically for code transformation. Our service takes:

Your original code
The desired update specification
And returns the transformed code while preserving structure and formatting

Here's a simple example:

# Original code
def add(a, b):
    return a + b

# After Morph (adding type hints)
def add(a: int, b: int) -> int:
    return a + b

Technical Implementation

Morph leverages several key optimizations to deliver fast and accurate code transformations:

Speculative Decoding with Input: We use your original code as speculation to accelerate the generation process
Continuous Batching: Adaptive batch formation with dynamic SLAs for optimal throughput
Optimized Inference: Custom CUDA kernels and inference graph optimization for maximum performance

Getting Started

Morph provides an OpenAI-compatible API that integrates seamlessly with existing tools:

import openai
import os

USER_PROMPT = """<code>{original_code}</code>
<update>{update_snippet}</update>"""

client = openai.OpenAI(
    api_key=os.getenv("MORPH_API_KEY"),
    base_url="https://api.morphllm.com/v1"
)

def execute_query(original_code, update_snippet):
    response = client.chat.completions.create(
        model="morph-v0",
        messages=[
            {
                "role": "user",
                "content": USER_PROMPT.format(
                    original_code=original_code,
                    update_snippet=update_snippet
                )
            }
        ]
    )
    return response.choices[0].message.content

# Example usage
original = """def add(a, b):
    return a + b"""
    
update = """//.. existing code ...
    return 2*a + 2*b"""

result = execute_query(original, update)
print(result)

Performance Features

Our architecture includes several optimizations:

Quantization-Aware KV Cache
- Mixed precision storage
- Intelligent cache management
- Dynamic precision switching
Inference Optimization
- Static analysis for redundancy elimination
- Optimal tensor layout planning
- Smart memory management
Request Processing
- Priority queuing for different client tiers
- Dynamic batch size adjustment
- Intelligent request routing

Next Steps

The long tail of Morph is the inference speed. We're always working on it.