---
title: "Just-in-Time Compilation"
description: "Learn how Codeflash optimizes code using JIT compilation with Numba, PyTorch, TensorFlow, and JAX"
icon: "bolt"
sidebarTitle: "JIT Compilation"
keywords: ["JIT", "just-in-time", "numba", "pytorch", "tensorflow", "jax", "GPU", "CUDA", "MPS", "compilation", "performance"]
---

# Just-in-Time Compilation

Just-in-time (JIT) compilation is a runtime technique where code is compiled into machine code on the fly, right before it is executed, to improve performance. Codeflash supports optimizing numerical code using Just-in-Time (JIT) compilation via leveraging JIT compilers from the **Numba**, **PyTorch**, **TensorFlow**, and **JAX** frameworks.

## How Codeflash Optimizes with JIT

When Codeflash identifies a function that could benefit from JIT compilation, it:

1. Rewrites the code in a JIT-compatible format, which may involve breaking down complex functions into separate JIT-compiled components.
2. Generates appropriate tests that are compatible with JIT-compiled code, carefully handling data types since JIT compilers have stricter input type requirements.
3. Disables JIT compilation when running coverage and tracer. This ensures accurate coverage and trace data, since both rely on Python bytecode execution. JIT-compiled code bypasses Python bytecode, so it would prevent proper tracking.
4. Disables the Line Profiler for JIT compiled code. It could be possible to disable JIT compilation and run the line profiler, but that would lead to inaccurate information which could misguide the optimization process.

## Configuration

JIT compilation support is **enabled automatically** in Codeflash. You don't need to modify any configuration to enable JIT-based optimizations. Codeflash will automatically detect when JIT compilation could improve performance and suggest appropriate optimizations.

## When JIT Compilation Helps

JIT compilation is most effective for:

- Numerical computations with loops that can't be easily vectorized.
- Custom algorithms not covered by existing optimized libraries.
- Functions that are called repeatedly with consistent input types.
- Code that benefits from hardware-specific optimizations (SIMD acceleration).

### Example

#### Function Definition

```python
import torch
def complex_activation(x):
    """A custom activation with many small operations - compile makes a huge difference"""
    # Many sequential element-wise ops create kernel launch overhead
    x = torch.sin(x)
    x = x * torch.cos(x)
    x = x + torch.exp(-x.abs())
    x = x / (1 + x.pow(2))
    x = torch.tanh(x) * torch.sigmoid(x)
    x = x - 0.5 * x.pow(3)
    return x
```

#### Benchmarking Snippet (replace `cuda` with `mps` to run on your Mac)

```python
import time
# Create compiled version
complex_activation_compiled = torch.compile(complex_activation)

# Benchmark
x = torch.randn(1000, 1000, device='cuda')

# Warmup steps are slower as the JIT compiler is understanding the function execution to compile into machine code
for _ in range(10):
    _ = complex_activation(x)
    _ = complex_activation_compiled(x)

# Time uncompiled
torch.cuda.synchronize()
start = time.time()
for _ in range(100):
    y = complex_activation(x)
torch.cuda.synchronize()
uncompiled_time = time.time() - start

# Time compiled
torch.cuda.synchronize()
start = time.time()
for _ in range(100):
    y = complex_activation_compiled(x)
torch.cuda.synchronize()
compiled_time = time.time() - start

print(f"Uncompiled: {uncompiled_time:.4f}s")
print(f"Compiled: {compiled_time:.4f}s")
print(f"Speedup: {uncompiled_time/compiled_time:.2f}x")
```

Expected Output on CUDA

```
Uncompiled: 0.0176s
Compiled: 0.0063s
Speedup: 2.80x
```

Here, JIT compilation via `torch.compile` is the only viable option because
1. Already vectorized - All operations are already PyTorch tensor ops.
2. Multiple Kernel Launches - Uncompiled code launches ~10 separate kernels. `torch.compile` fuses them into 1-2 kernels, eliminating kernel launch overhead.
3. No algorithmic improvement - The computation itself is already optimal.
4. Python overhead elimination - Removes Python interpreter overhead between operations.


## When JIT Compilation May Not Help

JIT compilation may not provide speedups when:

- The code already uses highly optimized libraries (e.g., `NumPy` with `MKL`, `cuBLAS`, `cuDNN`).
- Functions have variable input types or shapes that prevent effective compilation.
- The compilation overhead exceeds the runtime savings for short-running functions.
- The code relies heavily on Python objects or dynamic features that JIT compilers can't optimize.

### Example

#### Function Definition

```python
def adaptive_processing(x, threshold=0.5):
    """Function with data-dependent control flow - compile struggles here"""
    # Check how many values exceed threshold (data-dependent!)
    mask = x > threshold
    num_large = mask.sum().item()  # .item() causes graph break

    if num_large > x.numel() * 0.3:
        # Path 1: Many large values - use expensive operation
        result = torch.matmul(x, x.T)  # Already optimized by cuBLAS
        result = result.mean(dim=0)
    else:
        # Path 2: Few large values - use cheap operation
        result = x.mean(dim=1)

    return result
```

#### Benchmarking Snippet (replace `cuda` with `mps` to run on your Mac)

```python
# Create compiled version
adaptive_processing_compiled = torch.compile(adaptive_processing)

# Test with data that causes branch variation
x = torch.randn(500, 500, device='cuda')

# Warmup steps are slower as the JIT compiler is understanding the function execution to compile into machine code
for _ in range(10):
    _ = adaptive_processing(x)
    _ = adaptive_processing_compiled(x)

# Benchmark with varying data (causes recompilation)
torch.cuda.synchronize()
start = time.time()
for i in range(100):
    # Vary the data to trigger different branches
    x_test = torch.randn(500, 500, device='cuda') + (i % 2)
    y = adaptive_processing(x_test)
torch.cuda.synchronize()
uncompiled_time = time.time() - start

torch.cuda.synchronize()
start = time.time()
for i in range(100):
    x_test = torch.randn(500, 500, device='cuda') + (i % 2)
    y = adaptive_processing_compiled(x_test)  # Recompiles frequently!
torch.cuda.synchronize()
compiled_time = time.time() - start

print(f"Uncompiled: {uncompiled_time:.4f}s")
print(f"Compiled: {compiled_time:.4f}s")
print(f"Slowdown: {compiled_time/uncompiled_time:.2f}x")
```

Expected Output on CUDA

```
Uncompiled: 0.0296s
Compiled: 0.2847s
Slowdown: 9.63x
```

Why `torch.compile` is detrimental here:

1. Graph breaks - `.item()` forces a graph break, negating compile benefits.
2. Recompilation overhead - Different branches cause expensive recompilation each time.
3. Dynamic control flow - Data-dependent conditionals can't be optimized away.
4. Already optimized ops - `matmul` already uses `cuBLAS`; compile adds overhead without benefit.

#### Better Optimization Strategy

```python
def optimized_version(x, threshold=0.5):
    """Remove data-dependent control flow - vectorize instead"""
    mask = (x > threshold).float()
    weight = (mask.mean() > 0.3).float()  # Keep on GPU

    # Compute both paths, blend based on weight (branchless)
    expensive = torch.matmul(x, x.T).mean(dim=0)
    cheap = x.mean(dim=1).squeeze()

    # Pad cheap result to match expensive dimensions
    cheap_padded = cheap.expand(expensive.shape[0])

    result = weight * expensive + (1 - weight) * cheap_padded
    return result
```

Expected Output on CUDA

```
Optimized: 0.0277s
Speedup compared to Uncompiled: 1.57x
```

Key improvements:

1. Eliminate `.item()` - Keep computation on GPU.
2. Branchless execution - Compute both paths, blend results.
3. Vectorization - Replace conditionals with masked operations.
4. Reduce Python overhead - Minimize host-device synchronization.

## Supported JIT Frameworks

Each framework uses different compilation strategies to accelerate Python code:

### Numba (CPU Code)

Numba compiles Python functions to optimized machine code using the LLVM compiler infrastructure. Codeflash can suggest Numba optimizations that use:

- **`@jit`** - General-purpose JIT compilation with optional flags.
  - **`nopython=True`** - Compiles to machine code without falling back to the Python interpreter.
  - **`fastmath=True`** - Uses aggressive floating-point optimizations via LLVM's fastmath flag.
  - **`cache=True`** - cache compiled function to disk which reduces future runtimes.
  - **`parallel=True`** - Parallelizes code inside loops.

### PyTorch

PyTorch provides JIT compilation through `torch.compile()`, the recommended compilation API introduced in PyTorch 2.0. It uses TorchDynamo to capture Python bytecode and TorchInductor to generate optimized kernels.

- **`torch.compile()`** - Compiles a function or module for optimized execution.
  - **`mode`** - Controls the compilation strategy:
    - `"default"` - Balanced compilation with moderate optimization.
    - `"reduce-overhead"` - Minimizes Python overhead using CUDA graphs, ideal for small batches.
    - `"max-autotune"` - Spends more time auto-tuning to find the fastest kernels.
  - **`fullgraph=True`** - Requires the entire function to be captured as a single graph. Raises an error if graph breaks occur, useful for ensuring complete optimization.
  - **`dynamic=True`** - Enables dynamic shape support, allowing the compiled function to handle varying input sizes without recompilation.

### TensorFlow

TensorFlow uses `@tf.function` to compile Python functions into optimized TensorFlow graphs. When combined with XLA (Accelerated Linear Algebra), it can generate highly optimized machine code for both CPU and GPU.

- **`@tf.function`** - Converts Python functions into TensorFlow graphs for optimized execution.
  - **`jit_compile=True`** - Enables XLA compilation, which performs whole-function optimization including operation fusion, memory layout optimization, and target-specific code generation.

### JAX

JAX uses XLA to JIT compile pure functions into optimized machine code. It emphasizes functional programming patterns and captures side-effect-free operations for optimization.

- **`@jax.jit`** - JIT compiles functions using XLA with automatic operation fusion.