GitHub - DefTruth/ffpa-attn-mma: 📚FFPA: Yet antother Faster Flash Prefill Attention with O(1)⚡️SRAM complexity for headdim > 256, 1.8x~3x↑🎉faster vs SDPA EA.

🤖FFPA: Yet antother Faster Flash Prefill Attention with O(1)⚡️GPU SRAM complexity for large headdim🐑

📚FFPA L1~L3 Design | 📈L20 ~1.9x↑🎉 | 📈A30 ~1.8x↑🎉 | 📈3080 ~2.9x↑🎉 | 📈4090 ~2.1x↑🎉

🤖FFPA: 1.8x~3x🎉faster vs SDPA EA with or without MMA Acc F32

🤖[WIP] FFPA: Yet antother Faster Flash Prefill Attention with O(1) SRAM complexity & O(d/4) or O(1) register complexity for large headdim (D > 256), almost 1.8x~3x 🎉 faster than SDPA EA with or without MMA Acc F32 on many devices: 📈L20 ~1.9x↑🎉, 📈A30 ~1.8x↑🎉, 📈3080 ~2.9x↑🎉, 📈4090 ~2.1x↑🎉. FFPA Attention Algo: Fine-grained tiling for large headim, FA-2 Attention Algo: Coarse-grained tiling for small headidm.

💡NOTE: This project is still in its early dev stages and now provides some kernels and benchmarks for reference. More features will be added in the future. (Welcome to 🌟👆🏻star this repo to support me ~)

©️Citations🎉🎉

@misc{ffpa-attn-mma@2025,
  title={FFPA: Yet another Faster Flash Prefill Attention for large headdim.},
  url={https://github.com/DefTruth/ffpa-attn-mma.git},
  note={Open-source software available at https://github.com/DefTruth/ffpa-attn-mma.git},
  author={DefTruth etc},
  year={2025}
}

📖 FFPA L1~L3: FlashAttention + QKV Fine-grained Tiling at MMA level💡

We have extended FlashAttention for large headdim (D > 256) by implementing Fine-grained Tiling at the MMA level (GEMM style) for the Q@K^T and P@V matmul. This approach results in a constant SRAM usage of Br * 16 or Bc * 16 (Br = Bc) for Q, K, and V, leading to an overall SRAM complexity of O(2 * Br * 16) ≈ O(1) and a register complexity of O(d/4) or O(1). Consequently, this method allows us to extend headdim beyond 256 and achieve faster performance compared to SDPA with or without MMA Accumulation F32 (1.8x~3x 🎉 faster than SDPA EA).

We have named this new attention tiling technique FFPA: Faster Flash Prefill Attention. We have designed three (L1~L3) levels of FFPA based on SRAM and register complexity considerations. All levels will not introduce any additional VRAM requirements, ensuring that the HBM memory complexity remains same as FlashAttention. 👇

📚L1: level 1, O(2xBrx16)≈O(1) SRAM complexity, ≈O(d/4) register complexity.
📚L2: level 2, O(2xBrx16)≈O(1) SRAM complexity, ≈O(1) register complexity + Q@K^T recomputation.
📚L3: level 3, O(2xBrx16)≈O(1) SRAM complexity, ≈O(1) register complexity + scaling O via HBM offloading.

By leveraging this approach, we can achieve better performance than SDPA EA for large headdim (D > 256). Approximate SRAM and register complexity analysis for L1~L3 is as follows: (d=headdim, C,Br,Bc=Constant, Br=Bc) 👇

📚Complexity	📚FFPA L1	📚FFPA L2	📚FFPA L3	📚FA-2
SRAM	O(2xBrx16)≈O(1)	O(2xBrx16)≈O(1)	O(2xBrx16)≈O(1)	≈O(3xBrxd), d↑
Register	≈O(d/4), d↑	O((Bc/16)x4+2C)≈O(1)	O((Bc/16)x4+2C)≈O(1)	≈O(d/2), d↑
HBM	≈FA2≈O(Nd), O	≈FA2≈O(Nd), O	≈FA2≈O(Nd), O	≈O(Nd), O
Extra HBM	≈FA2≈O(N), m,l	≈FA2≈O(N), m,l	≈FA2≈O(N), m,l	≈O(N), m,l

📚👇Core Features🎉🎉: I have implemented FFPA L1~L3 using pure MMA PTX instructions, which supports many features such as Split-Q, SMEM Swizzle/Padding, QKV Multi-Stages(1~4), Tile MMAs/Warps, Mixed MMA F32/F16 Acc (Q@K^T MMA Acc F32 + P@V MMA Acc F16), Fully Shared QKV SMEM, Prefetch QKV g2s, Persist Q s2r/g2s, Fully QKV Fine-grained Tiling(GEMM style), Collective Store, etc.

📚Feature	📚Feature	📚Feature	📚Feature
✔️Tensor Cores	✔️MMA(m16n8k16)	✔️Tile Block(Br, Bc)	✔️Tile MMA/Warp
✔️Split Q(FA-2)	✔️Pack LDST(128 bits)	✔️SMEM Swizzle/Pad	✔️Copy Async
✔️Reg Double Buffers	✔️QKV Multi-Stages(1~4)	✔️Collective Store(Shfl)	✔️Prefetch QKV g2s
✔️QKV Fine-grained Tiling	✔️Shared QKV SMEM	✔️Mixed MMA Acc	✔️Persist Q s2r/g2s

📚 case: FFPA L1 kernel template signature: ffpa_attn_templates_L1.cuh

template<
  const int kHeadDim,              // Headdim, 32~1024     
  const int kMmaAtomM,             // MMA Atom M, 16
  const int kMmaAtomN,             // MMA Atom N, 8
  const int kMmaAtomK,             // MMA Atom K, 16
  const int kMmaTileSeqLenQ,       // 4, more MMA(warp), M=16*4=64, Q@K^T=[Br(M), d(K)]@[d(K),  Bc(N)]  
  const int kMmaTileSeqLenK,       // 1, more MMA(warp), N=8*1 =8,  Q@K^T=[Br(M), d(K)]@[d(K),  Bc(N)]    
  const int kMmaTileSeqLenP,       // 4, more MMA(warp), M=16*4=64, P@V  =[Br(M),Bc(K)]@[Bc(K), d(N) ]
  const int kMmaTileHeadDimV,      // 1, more MMA(warp), N=8*1 =8,  P@V  =[Br(M),Bc(K)]@[Bc(K), d(N) ]       
  const int kWarpTileSeqLenQ,      // 1, more values, M, Br=64*1=64, matmul M 
  const int kWarpTileSeqLenK,      // 8, more values, N, Bc=8*8 =64, matmul N
  const int kWarpTileSeqLenP,      // 1, more values, M, Br=64*1=64, matmul M
  const int kWarpTileHeadDimV,     // 8, more values, N, d=8*(1|2|3|4|...)=8|...|32|64|96|128|...
  const int kMmaAccFloat32QK,      // 0/1, Q@K^T, 0 MMA Acc with fp16, 1 MMA Acc with fp32.
  const int kMmaAccFloat32PV,      // 0/1, P@V, 0 MMA Acc with fp16, 1 MMA Acc with fp32.
  const int kOStorageAccFloat32,   // 0/1, MMA Acc always be f32/f16, but O storage can be fp32 or half.
  const int kPrefetchQK,           // Prefetch QK at the Appropriate Time Point. 
  const int kPrefetchPV,           // Prefetch V at the Appropriate Time Point. 
  const int kShareSmemQKV,         // QKV share the same shared memory, reuse QK smem for V.
  const int kPersistQs2r,          // Persist load Q s2r for headdim  < 512, more registers, but still keep O(1) SRAM.
  const int kPersistQg2s,          // Persist load Q g2s for headdim <= 320, more SRAM, but still keep register usage.
  const int kRegPipeKV,            // Registers Ping pong double buffers for ldmatrix s2r & mma computation overlapping.
  const int kStageQK,              // <= 4, may apply different multi stages policy for QK and V (<=4)
  const int kStagePV,              // <= 4, may apply different multi stages policy for QK and V (<=4)
  const int kPadQ,                 // Pad Q/K/V 0,8; 0 -> smem swizzle, > 0 -> padding
  const int kPadK,                 // Pad Q/K/V 0,8; 0 -> smem swizzle, > 0 -> padding
  const int kPadV                  // Pad Q/K/V 0,8; 0 -> smem swizzle, > 0 -> padding
> __global__ void // Q, K, V, O -> [B, H, N, D]
// FFPA Attention Algo: Fine-grained tiling at MMA level for large headdim (d>=256), 
// which can achieve 1.8x~3x🎉 faster than SDPA EA with or without MMA Acc F32.
ffpa_mma_stages_split_q_L1_large_d_template(half* Q, half* K, half* V, half* O, ...); 
// FA-2 Attention Algo: Coarse-grained tiling at Attention level for small headdim (d<256), 
// which can achieve 95%-150%🎉 performance as SDPA FA-2 BE with MMA Acc F32 for N<=4096, 
// and achieve almost 1.2x~1.4x🎉 faster than SDPA FA-2 via Mixed MMA Acc(Q@K^T F32 + 
// P@V F16) for all range N.
ffpa_mma_stages_split_q_L1_small_d_template(half* Q, half* K, half* V, half* O, ...);

📖 Prerequisites

Python >= 3.10
PyTorch >= 2.4.0, CUDA >= 12.4
flash-attention >= 2.6.3 (for test)
Recommended: PyTorch 2.5.1, CUDA 12.5
Docker: nvcr.io/nvidia/pytorch:24.10-py3

📖 Installation

The FFPA implemented in this repo can be install as a python library, namely, ffpa-attn library (optional).

git clone https://github.com/DefTruth/ffpa-attn-mma.git
# clone, then, run bash .dev/install.sh directly or run commands:
python3 setup.py bdist_wheel && cd dist && python3 -m pip install *.whl # pip uninstall ffpa-attn -y

📖 FFPA L1 (Level 1): Benchmark 🎉🎉

L1: level 1, O(2xBrx16)≈O(1) SRAM complexity, O(d/4) register complexity, the same GPU HBM memory complexity as FlashAttention. B=1, H=48, N=8192, D=320-1024(FA2 not supported 👀). (Notes, *=MMA Acc F32, ^=MMA Acc F16, Softmax Acc dtype is always be F32, T=TFLOPS, 👇Benchmark)

📚 NVIDIA L20 (*=MMA Acc F32, ^=MMA Acc F16, T=TFLOPS, ~1.8x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	56T	63T	58T	58T	55T	56T	54T	55T	54T	55T	54T	56T
FFPA L1*	102T	102T	103T	104T	103T	95T	95T	95T	95T	96T	95T	94T
Speedup	1.82x	1.62x	1.78x	1.79x	1.87x	1.7x	1.76x	1.73x	1.76x	1.75x	1.76x	1.68x
FFPA L1^	104T	103T	103T	102T	104T	103T	102T	94T	94T	94T	100T	100T
Speedup	1.86x	1.63x	1.78x	1.76x	1.89x	1.84x	1.89x	1.71x	1.74x	1.71x	1.85x	1.79x

📚 NVIDIA L20 (*=MMA Acc: QK F32 + PV F16, ^=MMA Acc F16, T=TFLOPS, ~1.9x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	56T	64T	58T	58T	55T	56T	54T	55T	54T	55T	54T	56T
FFPA L1*	105T	102T	104T	103T	105T	95T	95T	94T	94T	94T	102T	101T
Speedup	1.88x	1.59x	1.79x	1.78x	1.91x	1.7x	1.76x	1.71x	1.74x	1.71x	1.89x	1.8x
FFPA L1^	104T	103T	103T	102T	103T	103T	102T	94T	94T	94T	100T	100T
Speedup	1.86x	1.61x	1.78x	1.76x	1.87x	1.84x	1.89x	1.71x	1.74x	1.71x	1.85x	1.79x

📚 NVIDIA A30 (*=MMA Acc F32, ^=MMA Acc F16, T=TFLOPS, ~1.8x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	25T	25T	24T	24T	24T	24T	23T	22T	22T	22T	22T	18T
FFPA L1*	45T	44T	44T	43T	43T	38T	37T	37T	37T	36T	33T	32T
Speedup	1.8x	1.76x	1.83x	1.79x	1.79x	1.58x	1.61x	1.68x	1.68x	1.64x	1.5x	1.78x
FFPA L1^	48T	46T	45T	43T	44T	44T	44T	38T	37T	36T	40T	34T
Speedup	1.92x	1.84x	1.88x	1.79x	1.83x	1.83x	1.91x	1.73x	1.68x	1.64x	1.82x	1.89x

📚 NVIDIA A30 (*=MMA Acc: QK F32 + PV F16, ^=MMA Acc F16, T=TFLOPS, ~1.9x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	25T	25T	24T	24T	24T	24T	23T	22T	22T	22T	22T	18T
FFPA L1*	48T	46T	46T	43T	44T	38T	38T	38T	37T	36T	40T	34T
Speedup	1.92x	1.84x	1.92x	1.79x	1.83x	1.58x	1.65x	1.73x	1.68x	1.64x	1.82x	1.89x
FFPA L1^	48T	46T	45T	43T	44T	44T	44T	38T	37T	36T	39T	34T
Speedup	1.92x	1.84x	1.88x	1.79x	1.83x	1.83x	1.91x	1.73x	1.68x	1.64x	1.77x	1.89x

📚 NVIDIA RTX 3080 Laptop (*=MMA Acc F32, ^=MMA Acc F16, T=TFLOPS, ~2.5x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	13T	16T	11T	16T	15T	15T	15T	15T	14T	14T	14T	14T
FFPA L1*	33T	31T	30T	30T	30T	27T	27T	26T	26T	26T	26T	25T
Speedup	2.54x	1.94x	2.73x	1.88x	2.0x	1.8x	1.8x	1.73x	1.86x	1.86x	1.86x	1.79x
FFPA L1^	43T	41T	39T	39T	39T	39T	39T	36T	34T	33T	31T	33T
Speedup	3.31x	2.56x	3.55x	2.44x	2.6x	2.6x	2.6x	2.4x	2.43x	2.36x	2.21x	2.36x

📚 NVIDIA RTX 3080 Laptop (*=MMA Acc: QK F32 + PV F16, ^=MMA Acc F16, T=TFLOPS, ~2.9x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	13T	15T	12T	15T	14T	15T	14T	14T	14T	14T	14T	14T
FFPA L1*	38T	36T	34T	35T	34T	31T	32T	31T	30T	28T	27T	27T
Speedup	2.92x	2.4x	2.83x	2.33x	2.43x	2.07x	2.29x	2.21x	2.14x	2.0x	1.93x	1.93x
FFPA L1^	44T	41T	39T	39T	38T	39T	39T	36T	34T	32T	31T	33T
Speedup	3.38x	2.73x	3.25x	2.6x	2.71x	2.6x	2.79x	2.57x	2.43x	2.29x	2.21x	2.36x

📚 NVIDIA RTX 4090 (*=MMA Acc F32, ^=MMA Acc F16, T=TFLOPS, ~1.8x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	81T	94T	85T	85T	79T	81T	79T	80T	79T	80T	78T	78T
FFPA L1*	149T	150T	150T	150T	150T	140T	140T	140T	139T	139T	137T	134T
Speedup	1.84x	1.6x	1.76x	1.76x	1.9x	1.73x	1.77x	1.75x	1.76x	1.74x	1.76x	1.72x
FFPA L1^	194T	194T	189T	191T	197T	188T	184T	180T	177T	172T	171T	171T
Speedup	2.4x	2.06x	2.22x	2.25x	2.49x	2.32x	2.33x	2.25x	2.24x	2.15x	2.19x	2.19x

📚 NVIDIA RTX 4090 (*=MMA Acc: QK F32 + PV F16, ^=MMA Acc F16, T=TFLOPS, ~2.1x↑🎉)

Algorithm	320	384	448	512	576	640	704	768	832	896	960	1024
SDPA EA	82T	92T	85T	84T	78T	81T	79T	80T	78T	79T	77T	78T
FFPA L1*	176T	170T	171T	171T	171T	161T	160T	161T	160T	158T	165T	164T
Speedup	2.15x	1.85x	2.01x	2.04x	2.19x	1.99x	2.03x	2.01x	2.05x	2.0x	2.14x	2.1x
FFPA L1^	200T	191T	189T	191T	188T	188T	186T	179T	175T	173T	172T	170T
Speedup	2.44x	2.08x	2.22x	2.27x	2.41x	2.32x	2.35x	2.24x	2.24x	2.19x	2.23x	2.18x

📖 Python Testing

👇You can test many custom FFPA kernels via Python and figure out the difference in their performance. The --gen-bench and --plot options help you generate a benchmark table in Markdown style and speedup bar plots on your device. Contributions of your benchmark tables and plots are welcome via a PR 🎉🎉.

📚 case: B=1, H=48, N=8192, D=320(FA2 not supported)

# You can test on many devices, such as Volta, Ampere, Ada, Hopper, ...
cd tests && python3 test.py --B 1 --H 48 --N 8192 --show-all --D 320
---------------------------------------B=1, H=48, N=8192, D=320, Warmup: 1, Iters: 5--------------------
                   (sdpa): ['-0.02380371'], time:73.66518ms, TFLOPS:56.19 (+0.00 %)(~1.00x)
 (ffpa+acc+f32+L1+stage1): ['-0.02378845'], time:52.87361ms, TFLOPS:78.28 (+39.32%)(~1.39x)
 (ffpa+acc+f32+L1+stage2): ['-0.02378845'], time:40.84062ms, TFLOPS:101.35(+29.46%)(~1.80x)
 (ffpa+acc+f32+L1+stage3): ['-0.02378845'], time:40.49534ms, TFLOPS:102.21(+0.85 %)(~1.82x)
 (ffpa+acc+f32+L1+stage4): ['-0.02378845'], time:40.88177ms, TFLOPS:101.25(+0.00 %)(~1.80x)
 (ffpa+acc+f16+L1+stage1): ['-0.02378845'], time:53.43298ms, TFLOPS:77.46 (+0.00 %)(~1.38x)
 (ffpa+acc+f16+L1+stage2): ['-0.02378845'], time:39.76068ms, TFLOPS:104.10(+1.85 %)(~1.85x)
 (ffpa+acc+f16+L1+stage3): ['-0.02378845'], time:39.54901ms, TFLOPS:104.66(+0.54 %)(~1.86x)
 (ffpa+acc+f16+L1+stage4): ['-0.02378845'], time:41.06554ms, TFLOPS:100.79(+0.00 %)(~1.79x)
--------------------------------------------------------------------------------------------------------

📚 case: Generate benchmark table and speedup bar plots on Your device.

cd tests && pip install matplotlib && python3 test.py --gen-bench --show-all --plot

📚 case: Compare small headdim (d<256, e.g 64), FFPA-L1 vs SDPA FA-2 BE.

# Enable ffpa-attn small d kernel which using coarse-grained tiling method.
export ENABLE_FFPA_PERSIST_Q_G2S=1 && export ENABLE_FFPA_PERSIST_KV_G2S=1 
python3 test.py --B 1 --H 32 --N 1024 --check --show-all --D 64 # NVIDIA L20
---------------------------------------B=1, H=32, N=1024, D=64, Warmup: 1, Iters: 5--------------------
                   (sdpa): ['0.00802612'], time:0.148057ms, TFLOPS:59.14 (+0.00 %)(~1.00x)
 (ffpa+acc+f32+L1+stage1): ['0.00803375'], time:0.103807ms, TFLOPS:84.34 (+42.63%)(~1.43x)
 (ffpa+acc+f32+L1+stage2): ['0.00803375'], time:0.102233ms, TFLOPS:85.64 (+1.54 %)(~1.45x)
 (ffpa+acc+f32+L1+stage3): ['0.00803375'], time:0.102519ms, TFLOPS:85.40 (+0.00 %)(~1.44x)
 (ffpa+acc+f32+L1+stage4): ['0.00803375'], time:0.102043ms, TFLOPS:85.80 (+0.19 %)(~1.45x)
 (ffpa+acc+f16+L1+stage1): ['0.00795746'], time:0.104713ms, TFLOPS:83.61 (+0.00 %)(~1.41x)
 (ffpa+acc+f16+L1+stage2): ['0.00795746'], time:0.102949ms, TFLOPS:85.05 (+0.00 %)(~1.44x)
 (ffpa+acc+f16+L1+stage3): ['0.00795746'], time:0.108957ms, TFLOPS:80.36 (+0.00 %)(~1.36x)
 (ffpa+acc+f16+L1+stage4): ['0.00795746'], time:0.103282ms, TFLOPS:84.77 (+0.00 %)(~1.43x)
--------------------------------------------------------------------------------------------------------
python3 test.py --B 1 --H 32 --N 4096 --check --show-all --D 64 # NVIDIA L20
-------------------------B=1, H=32, N=4096, D=64, Warmup: 1, Iters: 5-----------------------------------
                   (sdpa): ['0.01959229'], time:1.397752ms, TFLOPS:100.24(+0.00 %)(~1.00x)
 (ffpa+acc+f32+L1+stage1): ['0.01959229'], time:1.368856ms, TFLOPS:102.36(+2.11 %)(~1.02x)
 (ffpa+acc+f32+L1+stage2): ['0.01959229'], time:1.367807ms, TFLOPS:102.44(+0.08 %)(~1.02x)
 (ffpa+acc+f32+L1+stage3): ['0.01959229'], time:1.367855ms, TFLOPS:102.43(+0.00 %)(~1.02x)
 (ffpa+acc+f32+L1+stage4): ['0.01959229'], time:1.368045ms, TFLOPS:102.42(+0.00 %)(~1.02x)
 (ffpa+acc+f16+L1+stage1): ['0.01957703'], time:1.389312ms, TFLOPS:100.85(+0.00 %)(~1.01x)
 (ffpa+acc+f16+L1+stage2): ['0.01957703'], time:1.388311ms, TFLOPS:100.92(+0.00 %)(~1.01x)
 (ffpa+acc+f16+L1+stage3): ['0.01957703'], time:1.386976ms, TFLOPS:101.02(+0.00 %)(~1.01x)
 (ffpa+acc+f16+L1+stage4): ['0.01957703'], time:1.387834ms, TFLOPS:100.96(+0.00 %)(~1.01x)
--------------------------------------------------------------------------------------------------------

💡NOTE: Please check all configurable environment variables in env.py.

©️License

GNU General Public License v3.0

🎉Contribute

How to contribute? Wecome to star⭐️ this repo to support me👆🏻 ~

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bench		bench
csrc		csrc
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include		include
tests		tests
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
env.py		env.py
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

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🤖FFPA: Yet antother Faster Flash Prefill Attention with O(1)⚡️GPU SRAM complexity for large headdim🐑

🤖FFPA: 1.8x~3x🎉faster vs SDPA EA with or without MMA Acc F32

©️Citations🎉🎉

📖 Contents

📖 FFPA L1~L3: FlashAttention + QKV Fine-grained Tiling at MMA level💡

📖 Prerequisites

📖 Installation

📖 FFPA L1 (Level 1): Benchmark 🎉🎉

📖 Python Testing

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🤖FFPA: Yet antother Faster Flash Prefill Attention with O(1)⚡️GPU SRAM complexity for large headdim🐑

🤖FFPA: 1.8x~3x🎉faster vs SDPA EA with or without MMA Acc F32

©️Citations🎉🎉

📖 Contents

📖 FFPA L1~L3: FlashAttention + QKV Fine-grained Tiling at MMA level💡

📖 Prerequisites

📖 Installation

📖 FFPA L1 (Level 1): Benchmark 🎉🎉

📖 Python Testing

©️License

🎉Contribute

📖 References

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