FlashAttention

This repository provides the official implementation of FlashAttention and
FlashAttention-2 from the
following papers.

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré
Paper: https://arxiv.org/abs/2205.14135
IEEE Spectrum article about our submission to the MLPerf 2.0 benchmark using FlashAttention.

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Tri Dao

Paper: https://tridao.me/publications/flash2/flash2.pdf

FlashAttention-2

Usage

We've been very happy to see FlashAttention being widely adopted in such a short
time after its release. This page
contains a partial list of places where FlashAttention is being used.

FlashAttention and FlashAttention-2 are free to use and modify (see LICENSE).
Please cite and credit FlashAttention if you use it.

FlashAttention-3 beta release

FlashAttention-3 is optimized for Hopper GPUs (e.g. H100).

Blogpost: https://tridao.me/blog/2024/flash3/

Paper: https://tridao.me/publications/flash3/flash3.pdf

FlashAttention-3 speedup on H100 80GB SXM5 with FP16

This is a beta release for testing / benchmarking before we integrate that with
the rest of the repo.

Currently released:

FP16 / BF16 forward and backward, FP8 forward

Requirements: H100 / H800 GPU, CUDA >= 12.3.

We highly recommend CUDA 12.8 for best performance.

To install:

cd hopper
python setup.py install

To run the test:

export PYTHONPATH=$PWD
pytest -q -s test_flash_attn.py

Once the package is installed, you can import it as follows:

import flash_attn_interface
flash_attn_interface.flash_attn_func()

Installation and features

Requirements:

CUDA toolkit or ROCm toolkit
PyTorch 2.2 and above.
packaging Python package (pip install packaging)
ninja Python package (pip install ninja) *
Linux. Might work for Windows starting v2.3.2 (we've seen a few positive reports) but Windows compilation still requires more testing. If you have ideas on how to set up prebuilt CUDA wheels for Windows, please reach out via Github issue.

* Make sure that ninja is installed and that it works correctly (e.g. ninja --version then echo $? should return exit code 0). If not (sometimes ninja --version then echo $? returns a nonzero exit code), uninstall then reinstall
ninja (pip uninstall -y ninja && pip install ninja). Without ninja,
compiling can take a very long time (2h) since it does not use multiple CPU
cores. With ninja compiling takes 3-5 minutes on a 64-core machine using CUDA toolkit.

To install:

pip install flash-attn --no-build-isolation

Alternatively you can compile from source:

python setup.py install

If your machine has less than 96GB of RAM and lots of CPU cores, ninja might
run too many parallel compilation jobs that could exhaust the amount of RAM. To
limit the number of parallel compilation jobs, you can set the environment
variable MAX_JOBS:

MAX_JOBS=4 pip install flash-attn --no-build-isolation

Interface: src/flash_attention_interface.py

NVIDIA CUDA Support

Requirements:

CUDA 12.0 and above.

We recommend the
Pytorch
container from Nvidia, which has all the required tools to install FlashAttention.

FlashAttention-2 with CUDA currently supports:

Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100). Support for Turing
GPUs (T4, RTX 2080) is coming soon, please use FlashAttention 1.x for Turing
GPUs for now.
Datatype fp16 and bf16 (bf16 requires Ampere, Ada, or Hopper GPUs).
All head dimensions up to 256. ~~Head dim > 192 backward requires A100/A800 or H100/H800~~. Head dim 256 backward now works on consumer GPUs (if there's no dropout) as of flash-attn 2.5.5.

AMD ROCm Support

ROCm version has two backends. There is composable_kernel (ck) which is the default backend and a Triton backend. They provide an implementation of FlashAttention-2.

Requirements:

ROCm 6.0 and above.

We recommend the
Pytorch
container from ROCm, which has all the required tools to install FlashAttention.

Composable Kernel Backend

FlashAttention-2 ROCm CK backend currently supports:

MI200 or MI300 GPUs.
Datatype fp16 and bf16
Both forward's and backward's head dimensions up to 256.

Triton Backend

The Triton implementation of the Flash Attention v2 is currently a work in progress.

It supports AMD's CDNA (MI200, MI300) and RDNA GPU's using fp16, bf16 and fp32 datatypes.

These features are supported in Fwd and Bwd

Fwd and Bwd with causal masking
Variable sequence lengths
Arbitrary Q and KV sequence lengths
Arbitrary head sizes
Multi and grouped query attention
Dropout
Rotary embeddings
ALiBi

We are working on the following things

Paged Attention
Sliding Window
FP8
Performance Improvements

Getting Started

To get started with the triton backend for AMD, follow the steps below.

First install the recommended Triton version

pip install triton==3.2.0

Then install Flash Attention with the flag FLASH_ATTENTION_TRITON_AMD_ENABLE set to "TRUE".

cd flash-attention
git checkout main_perf
FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" python setup.py install

To test that things are working, you can run our tests. These tests take hours so you don't need to run the full thing.

FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" pytest tests/test_flash_attn_triton_amd.py

You can use autotune for better performance by using this flag FLASH_ATTENTION_TRITON_AMD_AUTOTUNE="TRUE"

FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" FLASH_ATTENTION_TRITON_AMD_AUTOTUNE="TRUE" python $PATH_TO_CODE

Docker

You can also use the Dockerfile below which does the above steps on top of the latest rocm/pytorch image.

FROM rocm/pytorch:latest

WORKDIR /workspace

# install triton
RUN pip install triton==3.2.0

# install flash attention
ENV FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE"

RUN git clone https://github.com/ROCm/flash-attention.git &&\ 
    cd flash-attention &&\
    git checkout main_perf &&\
    python setup.py install

# set working dir
WORKDIR /workspace/flash-attention

To build the docker file

docker build -t fa_triton .

To run the docker image

docker run -it --network=host --user root --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ipc=host --shm-size 16G --device=/dev/kfd --device=/dev/dri fa_triton

How to use FlashAttention

The main functions implement scaled dot product attention (softmax(Q @ K^T *
softmax_scale) @ V):

from flash_attn import flash_attn_qkvpacked_func, flash_attn_func

flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False,
                          window_size=(-1, -1), alibi_slopes=None, deterministic=False):
"""dropout_p should be set to 0.0 during evaluation
If Q, K, V are already stacked into 1 tensor, this function will be faster than
calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
of the gradients of Q, K, V.
If window_size != (-1, -1), implements sliding window local attention. Query at position i
will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive.
Arguments:
    qkv: (batch_size, seqlen, 3, nheads, headdim)
    dropout_p: float. Dropout probability.
    softmax_scale: float. The scaling of QK^T before applying softmax.
        Default to 1 / sqrt(headdim).
    causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
    window_size: (left, right). If not (-1, -1), implements sliding window local attention.
    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to
        the attention score of query i and key j.
    deterministic: bool. Whether to use the deterministic implementation of the backward pass,
        which is slightly slower and uses more memory. The forward pass is always deterministic.
Return:
    out: (batch_size, seqlen, nheads, headdim).
"""

flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False,
                window_size=(-1, -1), alibi_slopes=None, deterministic=False):
"""dropout_p should be set to 0.0 during evaluation
Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
If window_size != (-1, -1), implements sliding window local attention. Query at position i
will only attend to keys between
[i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.

Arguments:
    q: (batch_size, seqlen, nheads, headdim)
    k: (batch_size, seqlen, nheads_k, headdim)
    v: (batch_size, seqlen, nheads_k, headdim)
    dropout_p: float. Dropout probability.
    softmax_scale: float. The scaling of QK^T before applying softmax.
        Default to 1 / sqrt(headdim).
    causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
    window_size: (left, right). If not (-1, -1), implements sliding window local attention.
    alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
        (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
        is added to the attention score of query i and key j.
    deterministic: bool. Whether to use the deterministic implementation of the backward pass,
        which is slightly slower and uses more memory. The forward pass is always deterministic.
Return:
    out: (batch_size, seqlen, nheads, headdim).
"""

def flash_attn_with_kvcache(
    q,
    k_cache,
    v_cache,
    k=None,
    v=None,
    rotary_cos=None,
    rotary_sin=None,
    cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
    cache_batch_idx: Optional[torch.Tensor] = None,
    block_table: Optional[torch.Tensor] = None,
    softmax_scale=None,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite context window
    rotary_interleaved=True,
    alibi_slopes=None,
):
    """
    If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
    k and v. This is useful for incremental decoding: you can pass in the cached keys/values from
    the previous step, and update them with the new keys/values from the current step, and do
    attention with the updated cache, all in 1 kernel.

    If you pass in k / v, you must make sure that the cache is large enough to hold the new values.
    For example, the KV cache could be pre-allocated with the max sequence length, and you can use
    cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.

    Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
    rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
    If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
    and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
    If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
    indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).

    See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.

    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.

    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
        1 1 1 1 0
        1 1 1 1 1
    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
        0 0
        0 0
        0 0
        1 0
        1 1
    If the row of the mask is all zero, the output will be zero.

    If window_size != (-1, -1), implements sliding window local attention. Query at position i
    will only attend to keys between
    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.

    Note: Does not support backward pass.

    Arguments:
        q: (batch_size, seqlen, nheads, headdim)
        k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no block_table,
            or (num_blocks, page_block_size, nheads_k, headdim) if there's a block_table (i.e. paged KV cache)
            page_block_size must be a multiple of 256.
        v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no block_table,
            or (num_blocks, page_block_size, nheads_k, headdim) if there's a block_table (i.e. paged KV cache)
        k [optional]: (batch_size, seqlen_new, nheads_k, headdim). If not None, we concatenate
            k with k_cache, starting at the indices specified by cache_seqlens.
        v [optional]: (batch_size, seqlen_new, nheads_k, headdim). Similar to k.
        rotary_cos [optional]: (seqlen_ro, rotary_dim / 2). If not None, we apply rotary embedding
            to k and q. Only applicable if k and v are passed in. rotary_dim must be divisible by 16.
        rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos.
        cache_seqlens: int, or (batch_size,), dtype torch.int32. The sequence lengths of the
            KV cache.
        block_table [optional]: (batch_size, max_num_blocks_per_seq), dtype torch.int32.
        cache_batch_idx: (batch_size,), dtype torch.int32. The indices used to index into the KV cache.
            If None, we assume that the batch indices are [0, 1, 2, ..., batch_size - 1].
            If the indices are not distinct, and k and v are provided, the values updated in the cache
                 might come from any of the duplicate indices.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
        rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in.
            If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
            rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
            (i.e. GPT-NeoX style).
        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
            (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
            is added to the attention score of query i and key j.

    Return:
        out: (batch_size, seqlen, nheads, headdim).
    """

To see how these functions are used in a multi-head attention layer (which
includes QKV projection, output projection), see the MHA implementation.

Changelog

2.0: Complete rewrite, 2x faster

Upgrading from FlashAttention (1.x) to FlashAttention-2

These functions have been renamed:

flash_attn_unpadded_func -> flash_attn_varlen_func
flash_attn_unpadded_qkvpacked_func -> flash_attn_varlen_qkvpacked_func
flash_attn_unpadded_kvpacked_func -> flash_attn_varlen_kvpacked_func

If the inputs have the same sequence lengths in the same batch, it is simpler
and faster to use these functions:

flash_attn_qkvpacked_func(qkv, dropout_p=0.0, softmax_scale=None, causal=False)

flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False)

2.1: Change behavior of causal flag

If seqlen_q != seqlen_k and causal=True, the causal mask is aligned to the
bottom right corner of the attention matrix, instead of the top-left corner.

For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 =
masked out) is:
v2.0:
1 0 0 0 0
1 1 0 0 0
v2.1:
1 1 1 1 0
1 1 1 1 1

If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
v2.0:
1 0
1 1
1 1
1 1
1 1
v2.1:
0 0
0 0
0 0
1 0
1 1
If the row of the mask is all zero, the output will be zero.

2.2: Optimize for inference

Optimize for inference (iterative decoding) when query has very small sequence
length (e.g., query sequence length = 1). The bottleneck here is to load KV
cache as fast as possible, and we split the loading across different thread
blocks, with a separate kernel to combine results.

See the function flash_attn_with_kvcache with more features for inference
(perform rotary embedding, updating KV cache inplace).

Thanks to the xformers team, and in particular Daniel Haziza, for this
collaboration.

2.3: Local (i.e., sliding window) attention

Implement sliding window attention (i.e., local attention). Thanks to Mistral
AI and in particular Timothée Lacroix for this
contribution. Sliding window was used in the Mistral 7B model.

2.4: ALiBi (attention with linear bias), deterministic backward pass.

Implement ALiBi (Press et al., 2021). Thanks to Sanghun Cho from Kakao Brain for this contribution.

Implement deterministic backward pass. Thanks to engineers from Meituan for this contribution.

2.5: Paged KV cache.

Support paged KV cache (i.e., PagedAttention).
Thanks to @beginlner for this contribution.

2.6: Softcapping.

Support attention with softcapping, as used in Gemma-2 and Grok models.
Thanks to @Narsil and @lucidrains for this contribution.

2.7: Compatibility with torch compile

Thanks to @ani300 for this contribution.

Performance

We present expected speedup (combined forward + backward pass) and memory savings from using FlashAttention against PyTorch standard attention, depending on sequence length, on different GPUs (speedup depends on memory bandwidth - we see more speedup on slower GPU memory).

We currently have benchmarks for these GPUs:

A100
H100

A100

We display FlashAttention speedup using these parameters:

Head dimension 64 or 128, hidden dimension 2048 (i.e. either 32 or 16 heads).
Sequence length 512, 1k, 2k, 4k, 8k, 16k.
Batch size set to 16k / seqlen.

Speedup

FlashAttention speedup on A100 80GB SXM5 with FP16/BF16

Memory

FlashAttention memory

We show memory savings in this graph (note that memory footprint is the same no matter if you use dropout or masking).
Memory savings are proportional to sequence length -- since standard attention has memory quadratic in sequence length, whereas FlashAttention has memory linear in sequence length.
We see 10X memory savings at sequence length 2K, and 20X at 4K.
As a result, FlashAttention can scale to much longer sequence lengths.

H100

FlashAttention speedup on H100 SXM5 with FP16/BF16

Full model code and training script

We have released the full GPT model
implementation.
We also provide optimized implementations of other layers (e.g., MLP, LayerNorm,
cross-entropy loss, rotary embedding). Overall this speeds up training by 3-5x
compared to the baseline implementation from Huggingface, reaching up to 225
TFLOPs/sec per A100, equivalent to 72% model FLOPs utilization (we don't need
any activation checkpointing).

We also include a training
script to
train GPT2 on Openwebtext and GPT3 on The Pile.

Triton implementation of FlashAttention

Phil Tillet (OpenAI) has an experimental implementation of FlashAttention in Triton:
https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py

As Triton is a higher-level language than CUDA, it might be easier to understand
and experiment with. The notations in the Triton implementation are also closer
to what's used in our paper.

We also have an experimental implementation in Triton that support attention
bias (e.g. ALiBi):
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/flash_attn_triton.py

Tests

We test that FlashAttention produces the same output and gradient as a reference
implementation, up to some numerical tolerance. In particular, we check that the
maximum numerical error of FlashAttention is at most twice the numerical error
of a baseline implementation in Pytorch (for different head dimensions, input
dtype, sequence length, causal / non-causal).

To run the tests:

pytest -q -s tests/test_flash_attn.py

When you encounter issues

This new release of FlashAttention-2 has been tested on several GPT-style
models, mostly on A100 GPUs.

If you encounter bugs, please open a GitHub Issue!

Tests

To run the tests:

pytest tests/test_flash_attn_ck.py

Citation

If you use this codebase, or otherwise found our work valuable, please cite:

@inproceedings{dao2022flashattention,
  title={Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
  author={Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2022}
}
@inproceedings{dao2023flashattention2,
  title={Flash{A}ttention-2: Faster Attention with Better Parallelism and Work Partitioning},
  author={Dao, Tri},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2024}
}

Dependencies of flash-attn (2).
Dependency	Optional
einops	false
torch	false

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