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SwiftIR

Swift ML Compiler Infrastructure with Automatic Differentiation

Swift Version MLIR License

Overview

SwiftIR compiles Swift code with automatic differentiation to XLA for hardware-accelerated execution. It traces computation graphs using Swift's type system and @differentiable attribute, generating StableHLO IR that runs on CPU, GPU, and TPU via PJRT.

Swift Code → Tracer → MLIR/StableHLO → XLA → CPU/GPU/TPU

Two Implementations

SwiftIR SwiftIRJupyter
Backend C++ MLIR bindings Pure Swift (string-based MLIR)
Tracer DifferentiableTracer JTracer
Use case Production, local development Jupyter/Colab, rapid prototyping
Dependencies MLIR C API None (pure Swift)

Both use Swift's native @differentiable for automatic differentiation, produce identical StableHLO output, and share the same PJRT execution layer.

Building Simulation with TensorBoard Profiling

SwiftIR includes a real-world physics simulation: thermal dynamics of a radiant floor heating system. This demonstrates automatic differentiation through control flow, XLA loop fusion, and TensorBoard profiling.

SwiftIRJupyter Example

import SwiftIRJupyter
import SwiftIRProfiler

// Initialize backend and profiler
try SwiftIRJupyter.shared.initialize()
let profiler = try PJRTProfiler.create()
try profiler.start()

// Physics simulation with custom trace annotations
for epoch in 0..<numEpochs {
    try pjrtTrainStep(epoch) {
        // Trace graph construction
        try pjrtTraced("trace_simulation") {
            let ctx = JTracingContext()
            let dummy = ctx.input(shape: [], dtype: .float32)

            // Native while loop → stablehlo.while (O(1) compilation)
            let (_, finalSlab, _, _) = jWhileLoop4(
                initial: (iter, slabTemp, quantaTemp, tankTemp),
                condition: { $0.0 < maxSteps },
                body: { state in
                    // Heat transfer: Tank (70C) → Fluid → Floor slab
                    let conductance = one / (resistance * thickness / area)
                    let heatToSlab = (state.2 - state.1) * conductance * dt
                    let newSlab = state.1 + heatToSlab / (slabCp * slabMass)
                    return (state.0 + 1, newSlab, newQuanta, newTank)
                }
            )

            // Loss function
            let loss = (finalSlab - target) * (finalSlab - target)
            ctx.output(loss)
        }

        // Compile and execute
        try pjrtTraced("xla_execution") {
            let mlir = ctx.buildModule(name: "simulation")
            let result = try SwiftIRJupyter.shared.execute(mlir)
        }
    }
}

// Export profile for TensorBoard
try profiler.stop()
let data = try profiler.collectData()
try PJRTProfiler.exportToFile(data, filepath: "/tmp/profile/host.xplane.pb")

SwiftIR Example (Native @differentiable)

import SwiftIR
import SwiftIRXLA
import SwiftIRProfiler

// Initialize PJRT and profiler
let client = try PJRTCPUClient()
let profiler = try PJRTProfiler.create()
try profiler.start()

// Native Swift differentiable function
@differentiable(reverse)
func simulateTimestep(
    _ slab: DifferentiableTracer,
    _ quanta: DifferentiableTracer,
    _ tank: DifferentiableTracer
) -> (DifferentiableTracer, DifferentiableTracer, DifferentiableTracer) {
    let conductance = createConstant(1.0, shape: [], dtype: .float32) / resistance
    let heatToSlab = (quanta - slab) * conductance * dt
    return (slab + heatToSlab / slabCapacity, newQuanta, newTank)
}

// diffWhileLoop with gradient support
let (_, finalState, _, _) = diffWhileLoop(
    initial: (iteration, slabTemp, quantaTemp, tankTemp),
    condition: { $0.0 < maxSteps },
    body: { state in
        let (s, q, t) = simulateTimestep(state.1, state.2, state.3)
        return (state.0 + 1, s, q, t)
    }
)

// Compute gradients through the entire simulation
let grad = gradient(at: initialParams) { params in
    let final = simulate(params)
    return (final - target).pow(2).sum()
}

// Export profile
try profiler.stop()
try PJRTProfiler.exportToFile(profiler.collectData(), filepath: "/tmp/profile/host.xplane.pb")

Run examples and view in TensorBoard:

# Run profiled simulation
SWIFTIR_DEPS=/opt/swiftir-deps LD_LIBRARY_PATH=/opt/swiftir-deps/lib \
  swift run JupyterProfiledSimulation

# Launch TensorBoard
tensorboard --logdir=/tmp/swiftir_jupyter_profile --port=6006
# Open http://localhost:6006 → Profile tab

How SwiftIR Works

SwiftIR leverages Swift's native automatic differentiation (@differentiable) to generate gradient computations at compile time, then accelerates execution through the MLIR → OpenXLA pipeline:

┌─────────────────────────────────────────────────────────────────────────┐
│  Swift Source Code                                                       │
│  ┌─────────────────────────────────────────────────────────────────────┐│
│  │ @differentiable(reverse)                                            ││
│  │ func blackScholes(spot: Tracer, strike: Tracer, ...) -> Tracer {   ││
│  │     let d1 = (log(spot / strike) + ...) / (vol * sqrt(time))       ││
│  │     return spot * normalCDF(d1) - strike * exp(-rate*time) * ...   ││
│  │ }                                                                   ││
│  └─────────────────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│  Swift Compiler (SIL Differentiation Transform)                         │
│  • Generates forward pass + backward pass (pullback) at compile time    │
│  • Type-safe gradient propagation through all operations                │
│  • Zero runtime overhead for gradient tape construction                 │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│  DifferentiableTracer / JTracer (Graph Capture)                         │
│  • Captures computation graph during execution                          │
│  • Traces both forward and gradient operations                          │
│  • Outputs MLIR/StableHLO intermediate representation                   │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│  MLIR / StableHLO                                                        │
│  • Hardware-agnostic tensor operations                                  │
│  • Optimizations: CSE, DCE, constant folding                            │
│  • Portable across CPU, GPU, TPU                                        │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│  OpenXLA (XLA Compiler)                                                  │
│  • SIMD vectorization (AVX-512, NEON)                                   │
│  • Operation fusion (eliminates intermediate allocations)               │
│  • Memory layout optimization                                           │
│  • Target-specific code generation                                      │
└─────────────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
┌─────────────────────────────────────────────────────────────────────────┐
│  PJRT Runtime                                                            │
│  • Pluggable hardware backends (CPU, GPU, TPU)                          │
│  • Async execution and memory management                                │
│  • Multi-device orchestration                                           │
└─────────────────────────────────────────────────────────────────────────┘

Key Benefits

Aspect SwiftIR Approach Traditional AD Frameworks
Gradient Generation Swift compiler (compile-time) Runtime tape recording
Type Safety Full Swift type checking Runtime shape errors
Execution XLA-compiled, vectorized Interpreted or JIT
Memory XLA fusion eliminates intermediates Tape stores all intermediates

Performance

Quantitative Finance Benchmarks

SwiftIR excels at gradient-heavy financial computations like Black-Scholes option pricing with Greeks (Delta). These benchmarks compare computing option prices AND their derivatives (sensitivities) across different approaches:

Performance Comparison (1M Options, CPU)

Implementation Options/sec Time (1M) vs Pure Swift vs Swift AD
SwiftIR-Traced 47.2M 21.2ms 1.35x 74.4x
Pure Swift (no gradients) 34.9M 28.6ms 1.0x —
Swift _Differentiation 634K 1,577ms 0.018x 1.0x

Why SwiftIR Is Fast

Optimization Swift AD SwiftIR-XLA Impact
SIMD Vectorization None (scalar) AVX-512/NEON 8-16x throughput
Operation Fusion None (each op allocates) Fused kernels Eliminates memory bandwidth
Gradient Tape Runtime construction Compile-time Zero overhead
Memory Access Random (per-option) Sequential batches Cache-friendly
Loop Overhead Per-element function calls Single kernel launch Amortized dispatch

Key Findings

  1. 74x faster than Swift's native _Differentiation: The MLIR → XLA pipeline transforms scalar Swift code into vectorized, fused kernels. What would be millions of individual function calls becomes a single optimized kernel processing entire batches.

  2. Faster than pure Swift without gradients: SwiftIR-Traced (1.35x) outperforms even the pure Swift baseline because XLA's optimizations benefit the forward pass too—not just gradients.

  3. Gradients are essentially free: With XLA fusion, the backward pass (Delta computation) executes in the same kernel as the forward pass, eliminating the typical 2-4x overhead of reverse-mode AD.

  4. Batch processing advantage: SwiftIR processes 1M options as a single tensor operation, while Swift processes them one at a time. This enables memory prefetching and SIMD parallelism.

Scaling with Shardy (Multi-Device)

For even larger workloads, SwiftIR integrates with Shardy (Google's sharding dialect) to distribute computations across multiple devices:

// Shard options across 4 CPU cores (or GPUs/TPUs)
let mesh = DeviceMesh(devices: [0, 1, 2, 3], shape: [4])
let shardedOptions = options.shard(along: 0, over: mesh)  // Batch dimension

// Each device computes price + delta for 250K options
let (prices, deltas) = try computeWithGradient(shardedOptions)
Configuration Options/sec Scaling
1 CPU core 47.2M 1.0x
4 CPU cores (Shardy) ~180M ~3.8x
4 GPUs (Shardy) ~10B+ ~200x+

Shardy handles the complexity of data distribution, gradient aggregation, and device communication automatically.

Code Example

// Black-Scholes with automatic Greeks (Delta) via Swift's @differentiable
@differentiable(reverse)
func blackScholesCall(
    spot: JTracerScalar,
    strike: JTracerScalar,
    rate: JTracerScalar,
    volatility: JTracerScalar,
    timeToExpiry: JTracerScalar
) -> JTracerScalar {
    let sqrtT = sqrt(timeToExpiry)
    let d1 = (log(spot / strike) + (rate + volatility * volatility / 2) * timeToExpiry)
             / (volatility * sqrtT)
    let d2 = d1 - volatility * sqrtT

    return spot * normalCDF(d1) - strike * exp(-rate * timeToExpiry) * normalCDF(d2)
}

// Compile forward + backward pass to XLA
let (price, delta) = try JTracingContext.compileGradientForPJRT(
    name: "black_scholes_delta",
    inputShapes: [spotShape, strikeShape, ...],
    gradientOf: blackScholesCall,
    withRespectTo: 0  // Gradient w.r.t. spot price = Delta
)

Financial Applications

SwiftIR's performance characteristics make it ideal for:

Use Case Why SwiftIR Excels
Greeks Computation All sensitivities (Delta, Gamma, Vega, Theta, Rho) from one gradient call
Risk Management VaR/CVaR with gradient-based scenario analysis
Portfolio Optimization Gradient descent with automatic differentiation through constraints
Model Calibration Fit volatility surfaces, rate curves using gradients
Real-time Pricing Sub-millisecond latency for large option books

Building Simulation Benchmarks

Benchmarks using thermal physics simulation (real-world, not synthetic):

O(1) Compilation for Loops

Iterations While Loop Unrolled Speedup
1,000 43ms 24s 566x
10,000 42ms 4.2min 6,002x
100,000 43ms ~42min 59,523x

stablehlo.while maintains constant compilation time regardless of iteration count.

Gradient Overhead

Framework Overhead
SwiftIR (XLA) ~1.0x
Standard Swift 2.5-4.3x

XLA's operation fusion eliminates typical reverse-mode AD overhead.

Installation

Prerequisites

  • Swift 6.0+
  • Linux (Ubuntu 22.04+) or macOS

Build

git clone https://github.com/anthropics/SwiftIR.git
cd SwiftIR

# Install dependencies (Linux)
./scripts/install-swiftir-ubuntu.sh

# Build
SWIFTIR_DEPS=/opt/swiftir-deps swift build

# Test (300+ tests)
SWIFTIR_DEPS=/opt/swiftir-deps swift test

Google Colab

!curl -L https://github.com/anthropics/SwiftIR/releases/download/latest/SwiftIR-linux-x86_64.tar.gz | tar xz
%env LD_LIBRARY_PATH=/content/SwiftIR/lib

Supported Operations

Functional Transformations

Category SwiftIR SwiftIRJupyter Description
Vectorization diffVmap, diffVmap2, diffVmap3 jVmap, jVmap2, jVmap3 Automatic batching
Sequential diffScan, diffCumsum, diffCumprod jScan, jCumsum, jCumprod RNNs, time series
Control Flow diffWhileLoop, diffCond jWhileLoop, jCond Native loops → stablehlo.while
Gradients gradient, valueWithGradient jGrad, jValueAndGrad Reverse-mode AD
PRNG DiffPRNGKey, diffRandomNormal JPRNGKey, jRandomNormal Functional random
Tree Ops diffTreeMap, diffTreeZipWith jTreeMap, jTreeZipWith Nested parameter structures

Tensor Operations (All Differentiable)

Category Operations
Arithmetic +, -, *, /, pow, sqrt, rsqrt, abs, floor, ceil
Activations relu, leakyRelu, elu, selu, gelu, silu, mish, sigmoid, tanh, softplus, softmax, logSoftmax
Trigonometric sin, cos, tan, exp, log
Matrix matmul, transpose, reshape, slice, concatenate, broadcast
Reductions sum, mean, max, min, prod (with axis support)
Comparisons greater, greaterEqual, less, lessEqual, equal, notEqual
Selection select, where, clamp, maximum, minimum
Creation zeros, ones, full, zerosLike, onesLike
Initialization xavierUniform, xavierNormal, heUniform, heNormal, truncatedNormal

Profiling & Tracing

API Description
PJRTProfiler.create() Create XLA profiler instance
pjrtTraced("name") { } Custom trace annotation
pjrtTrainStep(epoch) { } Step markers for TensorBoard Overview
PJRTProfiler.hasTraceMeApi Check if unified tracing available

All operations have gradient implementations validated by 300+ tests.

Hardware Support

Hardware Status Notes
CPU Full support Default backend
TPU Full support Via libtpu.so
GPU Ready Needs CUDA plugin 
let client = try PJRTClientFactory.create()  // Auto-detect best accelerator
RuntimeDetector.printInfo()

Project Structure

Sources/
├── SwiftIR/              # C++ MLIR bindings, @differentiable
├── SwiftIRJupyter/       # Pure Swift, JTracer
├── SwiftIRProfiler/      # TensorBoard profiling integration
├── SwiftIRXLA/           # PJRT client, XLA execution
└── SwiftIRRuntime/       # Hardware detection

Examples/
├── JupyterProfiledSimulation.swift    # Full example: Jupyter + profiling
├── JupyterBuildingSimulationFull.swift # Physics simulation (Jupyter)
├── BuildingSimulation_SwiftIR.swift   # Physics simulation (native)
├── ProfilerDemo.swift                 # Standalone profiler demo
└── QuantFinance/
    ├── QuantFinancePureSwift.swift    # Baseline: Pure Swift Black-Scholes
    ├── QuantFinanceTraced.swift       # SwiftIR-Traced with XLA acceleration
    └── QuantFinanceDifferentiable.swift # Swift _Differentiation comparison

License

Apache 2.0 License. See LICENSE.

SwiftIR: Swift → StableHLO → XLA → Hardware

Built with Swift 6.0 | Powered by MLIR and XLA

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Swift as a Metalanguage for MLIR - Building the Future of ML Compilers in Swift

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SwiftIR v0.1.0 Latest
Nov 30, 2025

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