README.md

Metal FlashAttention

A faster alternative to Metal Performance Shaders, a reference implementation of modern GPU algorithms, and a step toward defragmenting the AI ecosystem.

Algorithms:

• Attention
  • Dense (90.5% ALU)
  • Block-Sparse
• GEMM
  • FP16 (93.3% ALU)
  • FP32 (87.2% ALU)
  • Fused Biases

Usage

Progamming Language	MFA Supports	MPSGraph Supports	PyTorch Supports
CPU C++ (metal-cpp)	✅	❌	✅
GPU C++ (Indirect Command Buffers)	✅	❌	❌
Swift (iPadOS, Playgrounds)	✅	✅	❌
Swift (macOS, Xcode)	✅	✅	✅
Predecessor to Swift	not tested	✅	✅

Usage:

• Download Xcode 14.2 from the Apple developer tools archive
  • Copy into /Applications/Xcode 14.2.app, side by side with the existing Xcode installation /Applications/Xcode.app
• Run the Swift script to compile libMetalFlashAttention.metallib
  • Enter this repository from Terminal and type swift build.swift
• Read the API specification
• Generate Metal shader variants at runtime

Alternatively:

• Download the newest version of Xcode
• Fetch the Metal library from GitHub releases
• Run the unit tests from this repository

Performance

SGEMM, every square matrix from 1–1536:

HGEMM, every square matrix from 1–2048:

GEMM

Scaling by square size:

• Matrix M: every even integer
• Matrix N: every even integer
• Matrix K: every even integer
• For 2x batched, every multiple of 4
• For very large square matrices, granularity varies

Function Constant	Value
M_splits	2
N_splits	2
M_simd	Block M / M_splits
N_simd	Block N / N_splits
K_simd	Block K

Precision	Block M	Block N	Block K
Float32	32	32	32
Float32	48	48	24
Float16	32	32	32
Float16	48	48	32

Size Start	Size End	Duplicate Commands/Encoder	Trials
1	190	256	16
192	254	128	16
256	382	64	16
384	510	32	16
512	766	16	16
768	1022	8	16
1024	1534	4	16
1536	2048	2	16

Float32 Utilization (NN)

Float32 Utilization (NT)

Float32 Utilization (NT, Large)

Float16 Utilization (NN)

Float16 Utilization (NT, 2x Batched)

Float16 Utilization (NTN, 2x Batched, Bias)

Attention

Setup:

• Sequence dimension:
  • R = rows (output sequence length)
  • C = columns (input sequence length)
  • R = C
• Masking:
  • Only MFA supports block-sparse masks.
  • For "scaling by sparsity", sparse block size equals GEMM block size.

Scaling by sequence length:

• Masking:
  • No mask
  • Dense Mask: triangular mask
  • Sparse Mask: triangular mask, summarized by block-sparse mask
• Sequence length:
  • Small sequences: every multiple of 4
  • Large sequences: every multiple of 64
  • Causal mask: every even integer
• Head size: 64
• Head count:
  • Small sequences: 10
  • Large sequences: 5
  • Causal mask: 10

Scaling by head size:

• Masking: dense, no mask
• Sequence length 4096
• Head size: every integer
  • ≤64: every integer
  • >64: every roundUpToPowerOf2(D/64) integers
• Head count: 8

Function Constant	Value
Q_trans	❌
K_trans	✅
V_trans	❌
O_trans	❌
R_splits	TBD
R_simd	Block R / R_splits
C_simd	Block C
D_simd	$$8 \times \left \lceil{ \frac{D}{8} }\right \rceil $$

Float32 Sequence Scaling (Small)

Float16 Sequence Scaling (Small)

Dense: Stable Diffusion XL outermost attention layer @ 512x512 (sequence length = 1024)

Float16 Sequence Scaling (Large)

Dense: Stable Diffusion 2 outermost attention layer @ 512x512 (sequence length = 4096)

Float32 Sequence Scaling (Causal Mask)

Float16 Sequence Scaling (Causal Mask)

Float16 Head Scaling

Dense: Stable Diffusion 1 outermost attention layer @ 512x512 (head size = 40)

Roadmap

Releases:

• v0.1.0-alpha
  • Initial release, only non-batched GEMM without fused transposes
• v0.2.0-alpha
  • Fused transposes for A and B
  • Batched GEMM
• v1.0.0
  • Attention: dense and block-sparse
• v1.0.1
  • GEMM: fused biases

Prospective Future Goals:

• Tune the existing GEMM and Attention kernels for new A17/M3 hardware
• Kahan block-summation with double-single accumulate, in a manner portable to other vendors