JAX Compilation and XLA

JAX can automatically compile your JraphX code to optimized XLA (Accelerated Linear Algebra) programs, providing significant performance improvements.

XLA Compilation Benefits

XLA compilation in JAX provides several advantages for JraphX models:

Automatic optimization: XLA optimizes the entire computation graph
Cross-platform: Support for CPU, GPU, and TPU
Operator fusion: Combines multiple operations for better memory usage
Vectorization: Automatic SIMD optimization

import jax
import jax.numpy as jnp
from jraphx.nn.models import GCN
from flax import nnx

# Create model - XLA will optimize this automatically when JIT-compiled
model = GCN(
    in_features=64,
    hidden_features=128,
    out_features=32,
    num_layers=4,
    rngs=nnx.Rngs(42)
)

# JIT compilation triggers XLA optimization
@nnx.jit
def optimized_forward(model, x, edge_index):
    return model(x, edge_index)

# XLA optimizes the entire computation graph
x = jnp.ones((1000, 64))
edge_index = jnp.array([[0, 1, 2], [1, 2, 0]])
output = optimized_forward(model, x, edge_index)

Compilation vs. Eager Mode

JraphX models can run in both eager mode (for debugging) and compiled mode (for production):

# Eager mode - good for debugging
def debug_model(model, x, edge_index):
    print(f"Input shape: {x.shape}")
    output = model(x, edge_index)
    print(f"Output shape: {output.shape}")
    return output

# Compiled mode - good for production
@nnx.jit
def production_model(model, x, edge_index):
    return model(x, edge_index)

# Use debug mode during development
debug_output = debug_model(model, x, edge_index)

# Switch to compiled mode for performance
prod_output = production_model(model, x, edge_index)

Debugging Compiled Code

When debugging JIT-compiled JraphX models, you can:

Disable JIT temporarily:

with jax.disable_jit():
    output = optimized_forward(model, x, edge_index)  # Runs in eager mode

Use JAX debugging tools:

# Print intermediate values (only works in eager mode)
def debug_forward(model, x, edge_index):
    x = model.layers[0](x, edge_index)
    jax.debug.print("After layer 0: {}", x.shape)
    x = model.layers[1](x, edge_index)
    jax.debug.print("After layer 1: {}", x.shape)
    return x

Check compilation status:

# See the compiled computation graph
compiled_fn = jax.jit(production_model)
print(compiled_fn.lower(model, x, edge_index).compile().as_text())

Performance Comparison

Here’s how JraphX with JAX compilation compares to other approaches:

import time
import jax

# Measure compilation overhead (one-time cost)
start = time.time()
jit_fn = jax.jit(lambda m, x, e: m(x, e))
_ = jit_fn(model, x, edge_index)  # Compilation happens here
compile_time = time.time() - start
print(f"Compilation time: {compile_time:.2f}s")

# Measure runtime performance
start = time.time()
for _ in range(100):
    _ = model(x, edge_index).block_until_ready()  # Eager mode
eager_time = time.time() - start

start = time.time()
for _ in range(100):
    _ = jit_fn(model, x, edge_index).block_until_ready()  # Compiled mode
jit_time = time.time() - start

print(f"Eager mode: {eager_time:.3f}s")
print(f"JIT mode: {jit_time:.3f}s")
print(f"Speedup: {eager_time / jit_time:.2f}x")

For more information on JAX compilation and XLA, see the JAX compilation guide.