mps-flash-attn 0.1.4

Flash Attention for PyTorch on Apple Silicon (M1/M2/M3/M4)

MPS Flash Attention

Flash Attention for PyTorch on Apple Silicon (M1/M2/M3/M4).

O(N) memory instead of O(N²), enabling 8K+ sequence lengths on unified memory.

Features

• Forward pass: 2-5x faster than PyTorch SDPA
• Backward pass: Full gradient support for training
• Causal masking: Native kernel support (only 5% overhead)
• FP16/FP32: Native fp16 output (no conversion overhead)
• Pre-compiled kernels: Zero-compilation cold start (~6ms)

Performance

Tested on M1 Max, N=2048, B=4, H=8, D=64:

Operation	MPS Flash Attn	PyTorch SDPA	Speedup
Forward	5.3ms	15ms	2.8x
Forward+Backward	55ms	108ms	2.0x
Memory	80MB	592MB	7.4x less

Installation

Prerequisites

• macOS 14+ (Sonoma) or macOS 15+ (Sequoia)
• Xcode Command Line Tools (xcode-select --install)
• Python 3.10+ with PyTorch 2.0+

Build from source

# Clone with submodules
git clone --recursive https://github.com/mpsops/mps-flash-attention.git
cd mps-flash-attention

# Build Swift bridge
cd swift-bridge
swift build -c release
cd ..

# Install Python package
pip install -e .

Set environment variable

export MFA_BRIDGE_PATH=/path/to/mps-flash-attention/swift-bridge/.build/release/libMFABridge.dylib

Usage

Basic usage

from mps_flash_attn import flash_attention

# Standard attention (B, H, N, D)
q = torch.randn(2, 8, 4096, 64, device='mps', dtype=torch.float16)
k = torch.randn(2, 8, 4096, 64, device='mps', dtype=torch.float16)
v = torch.randn(2, 8, 4096, 64, device='mps', dtype=torch.float16)

out = flash_attention(q, k, v)

Causal masking (for autoregressive models)

out = flash_attention(q, k, v, is_causal=True)

Training with gradients

q.requires_grad = True
k.requires_grad = True
v.requires_grad = True

out = flash_attention(q, k, v, is_causal=True)
loss = out.sum()
loss.backward()  # Computes dQ, dK, dV

Drop-in replacement for SDPA

from mps_flash_attn import replace_sdpa

# Monkey-patch F.scaled_dot_product_attention
replace_sdpa()

# Now all attention ops use Flash Attention on MPS

Architecture

+----------------------------------------------------------+
|                    Python API                            |
|              mps_flash_attn/__init__.py                  |
|         (flash_attention, autograd Function)             |
+----------------------------+-----------------------------+
                             |
+----------------------------v-----------------------------+
|                 C++ Extension                            |
|            mps_flash_attn/csrc/mps_flash_attn.mm         |
|    (PyTorch bindings, MTLBuffer handling, offsets)       |
+----------------------------+-----------------------------+
                             | dlopen + dlsym
+----------------------------v-----------------------------+
|                 Swift Bridge                             |
|         swift-bridge/Sources/MFABridge/                  |
|   (MFABridge.swift, MetallibCache.swift)                 |
|   @_cdecl exports: mfa_init, mfa_create_kernel,          |
|                    mfa_forward, mfa_backward             |
+----------------------------+-----------------------------+
                             |
+----------------------------v-----------------------------+
|              Metal Flash Attention                       |
|    metal-flash-attention/Sources/FlashAttention/         |
|     (AttentionDescriptor, AttentionKernel, etc.)         |
|                                                          |
|   Generates Metal shader source at runtime,              |
|   compiles to .metallib, caches pipelines                |
+----------------------------------------------------------+

Project Structure

mps-flash-attention/
├── mps_flash_attn/              # Python package
│   ├── __init__.py              # Public API (flash_attention, replace_sdpa)
│   ├── csrc/
│   │   └── mps_flash_attn.mm    # PyTorch C++ extension
│   └── kernels/                 # Pre-compiled metallibs (optional)
│
├── swift-bridge/                # Swift -> C bridge
│   ├── Package.swift
│   └── Sources/MFABridge/
│       ├── MFABridge.swift      # C-callable API (@_cdecl)
│       └── MetallibCache.swift  # Disk caching for metallibs
│
├── metal-flash-attention/       # Upstream (git submodule)
│   └── Sources/FlashAttention/
│       └── Attention/
│           ├── AttentionDescriptor/  # Problem configuration
│           ├── AttentionKernel/      # Metal shader generation
│           └── ...
│
├── scripts/
│   └── build_metallibs.py       # Pre-compile kernels for distribution
│
└── setup.py                     # Python package setup

Changes from upstream metal-flash-attention

We made the following modifications to metal-flash-attention:

1. macOS 15+ compatibility (MTLLibraryCompiler.swift)

Apple restricted __asm in runtime-compiled Metal shaders on macOS 15. We added a fallback that uses xcrun metal CLI compilation when runtime compilation fails.

2. Causal masking support

Added causal flag to AttentionDescriptor and kernel generation:

• AttentionDescriptor.swift: Added causal: Bool property
• AttentionKernelDescriptor.swift: Added causal: Bool property
• AttentionKernel.swift: Added causal field
• AttentionKernel+Softmax.swift: Added maskCausal() function
• AttentionKernel+Source.swift: Added causal masking to forward/backward loops

Next Steps

1. PR to upstream metal-flash-attention

The macOS 15 fix and causal masking should be contributed back:

cd metal-flash-attention
git checkout -b macos15-causal-support
# Commit changes to:
#   - Sources/FlashAttention/Utilities/MTLLibraryCompiler.swift (new file)
#   - Sources/FlashAttention/Attention/AttentionDescriptor/*.swift
#   - Sources/FlashAttention/Attention/AttentionKernel/*.swift
git push origin macos15-causal-support
# Open PR at https://github.com/philipturner/metal-flash-attention

2. Publish mps-flash-attention to PyPI

# Add pyproject.toml with proper metadata
# Build wheel with pre-compiled Swift bridge
python -m build
twine upload dist/*

3. Pre-compile kernels for zero cold start

python scripts/build_metallibs.py
# Copies metallibs to mps_flash_attn/kernels/
# These get shipped with the wheel

Current Status (Jan 2025)

Working:

• Forward pass (fp16/fp32)
• Backward pass (dQ, dK, dV gradients)
• Causal masking
• Metallib disk caching
• Pipeline binary caching (MTLBinaryArchive)

Known limitations:

• Sequence length must be divisible by block size (typically 64)
• Head dimension: Best with 32, 64, 96, 128
• No arbitrary attention masks (only causal or none)
• No dropout

Credits

• metal-flash-attention by Philip Turner
• Flash Attention paper by Tri Dao et al.

License

MIT