# flash-linear-attention **Repository Path**: HAYPO/flash-linear-attention ## Basic Information - **Project Name**: flash-linear-attention - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-04-21 - **Last Updated**: 2026-04-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
# 💥 Flash Linear Attention [![hf_model](https://img.shields.io/badge/-Models-gray.svg?logo=huggingface&style=flat-square)](https://huggingface.co/fla-hub) [![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white&style=flat-square)](https://discord.gg/vDaJTmKNcS)
This repo provides efficient implementations for emerging model architectures, with a focus on efficient sequence modeling (e.g., linear attention, state space models, and their hybrids). **All implementations are written purely in PyTorch and Triton, making them platform-agnostic.** Currently verified platforms include NVIDIA, AMD, and Intel. **Any pull requests are welcome!**
Flash Linear Attention
* [News](#news) * [Models](#models) * [Installation](#installation) * [Usage](#usage) * [Token Mixing](#token-mixing) * [Fused Modules](#fused-modules) * [Generation](#generation) * [Hybrid Models](#hybrid-models) * [Training](#training) * [Evaluation](#evaluation) * [Benchmarks](#benchmarks) * [Citation](#citation) * [Star History](#star-history) * [Acknowledgements](#acknowledgements) ## News - [2026-04] 🧱 Add [MoBA](https://arxiv.org/abs/2502.13189) (Mixture of Block Attention) implementation to `fla`, with [FlashMoBA](https://github.com/mit-han-lab/flash-moba) backend support. - [2026-04] 🧱 Add [TileLang](https://github.com/tile-ai/tilelang) backend support for selected kernels. - [2026-04] 🎯 Add [GPT-OSS](https://openai.com/index/introducing-gpt-oss/)-style attention sink support to `fla`'s attention kernels. - [2026-03] 🚀 Add [Context Parallel](fla/ops/cp/README.md) support for KDA and GDN, enabling efficient distributed training across sequence dimension. - [2025-10] 🌘 Add Kimi Delta Attention (KDA) implementation to `fla` ([paper](https://arxiv.org/abs/2510.26692)). - [2025-09] 🌲 Add DeltaFormer implementation to `fla` ([paper](https://arxiv.org/abs/2505.19488v1)). - [2025-09] 🐻 Thrilled to announce that [GDN](fla/ops/gated_delta_rule) has been integrated into Qwen3-Next. Check out their [blog post](https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list) for more info! - [2025-08] 🌲 Add Log-Linear Attention implementation to `fla` ([paper](https://arxiv.org/abs/2506.04761)). - [2025-08] 🎓 Add MoM implementation to `fla` ([paper](https://arxiv.org/abs/2502.13685)). - [2025-07] 🐳 Add MLA implementation to `fla` ([paper](https://arxiv.org/abs/2405.04434)). - [2025-07] 🛣️ Add PaTH Attention implementation to `fla` ([paper](https://arxiv.org/abs/2505.16381)). - [2025-06] 🎉 Add MesaNet implementation to `fla` ([paper](https://arxiv.org/abs/2506.05233)). - [2025-06] 🐍 Add Comba implementation to `fla` ([paper](https://arxiv.org/abs/2506.02475)). - [2025-05] 🎉 Add Rodimus* implementation to `fla` ([paper](https://arxiv.org/abs/2410.06577)).
Older news - [2025-04] 🎉 Add DeltaProduct implementation to `fla` ([paper](https://arxiv.org/abs/2502.10297)). - [2025-04] 🎉 Add FoX implementation to `fla` ([paper](https://arxiv.org/abs/2503.02130)). - [2025-03] ~~We have changed the default `initializer_range` to the magic 🐳 0.006~~ The `initializer_range` was rolled back to the default value of 0.02. For actual training, we recommend trying both. - [2025-02] 🐳 Add NSA implementations to `fla`. See kernels [here](fla/ops/nsa). - [2025-01] 🔥 We are migrating to `torchtitan`-based training framework. Check out the [flame](https://github.com/fla-org/flame) repo for more details. - [2025-01] 🦅 Add RWKV7 implementations (both kernels and models) to `fla`. - [2024-12] Add `flash-bidirectional-attention` to `fla-org` ([repo](https://github.com/fla-org/flash-bidirectional-linear-attention)). - [2024-12] 🎉 Add Gated DeltaNet implementation to `fla` ([paper](https://arxiv.org/abs/2412.06464)). - [2024-12] 🚀 `fla` now officially supports kernels with variable-length inputs. - [2024-11] The inputs are now switched from head-first to seq-first format. - [2024-11] 💥 `fla` now provides a flexible way for training hybrid models. - [2024-10] 🔥 Announcing `flame`, a minimal and scalable framework for training `fla` models. Check out the details [here](https://github.com/fla-org/flame). - [2024-09] `fla` now includes a fused linear and cross-entropy layer, significantly reducing memory usage during training. - [2024-09] 🎉 Add GSA implementation to `fla` ([paper](https://arxiv.org/abs/2409.07146)). - [2024-05] 🎉 Add DeltaNet implementation to `fla` ([paper](https://arxiv.org/abs/2102.11174)). - [2024-05] 💥 `fla` v0.1: a variety of subquadratic kernels/layers/models integrated (RetNet/GLA/Mamba/HGRN/HGRN2/RWKV6, etc., see [Models](#models)). - [2023-12] 💥 Launch `fla`, offering a collection of implementations for state-of-the-art linear attention models.
## Models | Year | Venue | Model | Paper | Code | | | :--- | :------ | :------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------: | | 2022 | ACL | ABC | [ABC: Attention with Bounded-memory Control](https://arxiv.org/abs/2110.02488) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/abc.py) | | 2023 | | RetNet | [Retentive network: a successor to transformer for large language models](https://arxiv.org/abs/2307.08621) | [official](https://github.com/microsoft/torchscale/tree/main) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/multiscale_retention.py) | | 2023 | NeurIPS | HGRN | [Hierarchically Gated Recurrent Neural Network for Sequence Modeling](https://openreview.net/forum?id=P1TCHxJwLB) | [official](https://github.com/OpenNLPLab/HGRN) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/hgrn.py) | | 2024 | ICML | GLA | [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635) | [official](https://github.com/berlino/gated_linear_attention) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/gla.py) | | 2024 | ICML | Based | [Simple linear attention language models balance the recall-throughput tradeoff](https://arxiv.org/abs/2402.18668) | [official](https://github.com/HazyResearch/based) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/based.py) | | 2024 | ACL | Rebased | [Linear Transformers with Learnable Kernel Functions are Better In-Context Models](https://arxiv.org/abs/2402.10644) | [official](https://github.com/corl-team/rebased/) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/rebased.py) | | 2024 | NeurIPS | DeltaNet | [Parallelizing Linear Transformers with Delta Rule over Sequence Length](https://arxiv.org/abs/2406.06484) | [official](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/delta_net.py) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/delta_net.py) | | 2024 | COLM | HGRN2 | [HGRN2: Gated Linear RNNs with State Expansion](https://arxiv.org/abs/2404.07904) | [official](https://github.com/OpenNLPLab/HGRN2) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/hgrn2.py) | | 2024 | COLM | RWKV6 | [Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence](https://arxiv.org/abs/2404.05892) | [official](https://github.com/RWKV/RWKV-LM) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/rwkv6.py) | | 2024 | | LightNet | [You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet](https://arxiv.org/abs/2405.21022) | [official](https://github.com/OpenNLPLab/LightNet) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/lightnet.py) | | 2024 | ICML | Mamba2 | [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://arxiv.org/abs/2405.21060) | [official](https://github.com/state-spaces/mamba) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/models/mamba2) | | 2024 | NeurIPS | GSA | [Gated Slot Attention for Efficient Linear-Time Sequence Modeling](https://arxiv.org/abs/2409.07146) | [official](https://github.com/fla-org/flash-linear-attention/tree/main/fla/models/gsa) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/models/gsa) | | 2024 | | MLA | [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) | [official](https://github.com/deepseek-ai/DeepSeek-V2) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/mla.py) | | 2025 | ICLR | Samba | [Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling](https://arxiv.org/abs/2406.07522) | [official](https://github.com/microsoft/Samba) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/models/samba) | | 2025 | ICLR | Gated DeltaNet | [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://arxiv.org/abs/2412.06464) | [official](https://github.com/NVlabs/GatedDeltaNet) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/gated_delta_rule) | | 2025 | | RWKV7 | [RWKV-7 "Goose" with Expressive Dynamic State Evolution](https://arxiv.org/abs/2503.14456) | [official](https://github.com/BlinkDL/RWKV-LM/tree/main/RWKV-v7) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/rwkv7) | | 2025 | | NSA | [Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention](https://arxiv.org/abs/2502.11089) | | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/nsa) | | 2025 | ICLR | FoX | [Forgetting Transformer: Softmax Attention with a Forget Gate](https://arxiv.org/abs/2503.02130) | [official](https://github.com/zhixuan-lin/forgetting-transformer) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/forgetting_attn) | | 2025 | | DeltaProduct | [DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products](https://arxiv.org/abs/2502.10297) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/gated_deltaproduct.py) | | 2025 | ICLR | Rodimus* | [Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions](https://arxiv.org/abs/2410.06577) | [official](https://github.com/codefuse-ai/rodimus) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/rodimus.py) | | 2025 | | MesaNet | [MesaNet: Sequence Modeling by Locally Optimal Test-Time Training](https://arxiv.org/abs/2506.05233) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/mesa_net.py) | | 2025 | | Comba | [Comba: Improving Bilinear RNNs with Closed-loop Control](https://arxiv.org/abs/2506.02475) | [official](https://github.com/AwesomeSeq/Comba-triton) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/comba.py) | | 2025 | | PaTH | [PaTH Attention: Position Encoding via Accumulating Householder Transformations](https://arxiv.org/abs/2505.16381) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/path_attn.py) | | 2025 | | MoM | [MoM: Linear Sequence Modeling with Mixture-of-Memories](https://arxiv.org/abs/2502.13685) | [official](https://github.com/OpenSparseLLMs/MoM) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/mom.py) | | 2025 | | Log-Linear Attention | [Log-Linear Attention](https://arxiv.org/abs/2506.04761) | [official](https://github.com/HanGuo97/log-linear-attention) | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/log_linear_attn) | | 2025 | | DeltaFormer | [Understanding Transformer from the Perspective of Associative Memory](https://arxiv.org/abs/2505.19488v1) | | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/deltaformer.py) | | 2025 | | KDA | [Kimi Linear: An Expressive, Efficient Attention Architecture](https://arxiv.org/abs/2510.26692) | | [fla](https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/kda) | | 2025 | | MoBA | [MoBA: Mixture of Block Attention for Long-Context LLMs](https://arxiv.org/abs/2502.13189) | [official](https://github.com/MoonshotAI/MoBA) | [fla](https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/moba.py) | ## Installation [![nvidia-4090-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-4090.yml/badge.svg?branch=main&event=push)](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-4090.yml) [![nvidia-h100-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-h100.yml/badge.svg?branch=main&event=push)](https://github.com/fla-org/flash-linear-attention/actions/workflows/nvidia-h100.yml) [![intel-b580-ci](https://github.com/fla-org/flash-linear-attention/actions/workflows/intel-b580.yml/badge.svg?event=push)](https://github.com/fla-org/flash-linear-attention/actions/workflows/intel-b580.yml) The following requirements should be satisfied - [PyTorch](https://pytorch.org/) >= 2.7.0 - [Triton](https://github.com/triton-lang/triton) >= 3.3 (or nightly version, see [FAQs](FAQs.md)) - [einops](https://einops.rocks/) - [transformers](https://github.com/huggingface/transformers) >=4.45.0 - [datasets](https://github.com/huggingface/datasets) >=3.3.0 Starting from v0.3.2, the packages published on PyPI are `fla-core` and `flash-linear-attention`. The former contains all our customized kernels and only depends on PyTorch, Triton, and einops. The latter is an extension package of the former, containing `fla/layers` and `fla/models`, and depends on transformers. We also provide Triton implementations for conv1d operations, so causal-conv1d is not required. You can install `fla` with pip: ```sh pip install flash-linear-attention ``` As `fla` is actively developed now, for the latest features and updates, an alternative way is to install the package from source. Note that installing from git uses the default mode, so you need to uninstall both `fla-core` and `flash-linear-attention` first: ```sh # uninstall both packages first to ensure a successful upgrade pip uninstall fla-core flash-linear-attention -y && pip install -U git+https://github.com/fla-org/flash-linear-attention ``` or manage `fla` with submodules ```sh git submodule add https://github.com/fla-org/flash-linear-attention.git 3rdparty/flash-linear-attention ln -s 3rdparty/flash-linear-attention/fla fla ``` > [!NOTE] > For AMD GPUs, make sure to install the [Triton ROCm backend](https://github.com/triton-lang/triton). For Intel GPUs, use the [Triton XPU backend](https://github.com/intel/intel-xpu-backend-for-triton). See [FAQs](FAQs.md) for more details. If you have installed `triton-nightly` and `torch` pre-release version, please use the following command: ```sh pip install einops ninja datasets transformers numpy # uninstall both packages first to ensure a successful upgrade pip uninstall fla-core flash-linear-attention -y && pip install -U --no-use-pep517 git+https://github.com/fla-org/flash-linear-attention --no-deps ``` ## Usage ### Token Mixing We provide "token mixing" linear attention layers in `fla.layers` for you to use. You can replace the standard multihead attention layer in your model with other linear attention layers. Example usage is as follows: ```py >>> import torch >>> from fla.layers import MultiScaleRetention >>> batch_size, num_heads, seq_len, hidden_size = 32, 4, 2048, 1024 >>> device, dtype = 'cuda:0', torch.bfloat16 >>> retnet = MultiScaleRetention(hidden_size=hidden_size, num_heads=num_heads).to(device=device, dtype=dtype) >>> x = torch.randn(batch_size, seq_len, hidden_size).to(device=device, dtype=dtype) >>> y, *_ = retnet(x) >>> y.shape torch.Size([32, 2048, 1024]) ``` We provide the implementations of models that are compatible with 🤗 Transformers library. Here's an example of how to initialize a GLA model from the default configs in `fla`: ```py >>> from fla.models import GLAConfig >>> from transformers import AutoModelForCausalLM >>> config = GLAConfig() >>> model = AutoModelForCausalLM.from_config(config) ```
Click to expand config and model structure ```py >>> config GLAConfig { "attn": null, "attn_mode": "chunk", "bos_token_id": 1, "clamp_min": null, "conv_size": 4, "elementwise_affine": true, "eos_token_id": 2, "expand_k": 0.5, "expand_v": 1, "feature_map": null, "fuse_cross_entropy": true, "fuse_norm": true, "fuse_swiglu": true, "hidden_act": "swish", "hidden_ratio": 4, "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": null, "max_position_embeddings": 2048, "model_type": "gla", "norm_eps": 1e-06, "num_heads": 4, "num_hidden_layers": 24, "num_kv_heads": null, "tie_word_embeddings": false, "transformers_version": "4.50.1", "use_cache": true, "use_gk": true, "use_gv": false, "use_output_gate": true, "use_short_conv": false, "vocab_size": 32000 } >>> model GLAForCausalLM( (model): GLAModel( (embeddings): Embedding(32000, 2048) (layers): ModuleList( (0-23): 24 x GLABlock( (attn_norm): RMSNorm(2048, eps=1e-06) (attn): GatedLinearAttention( (q_proj): Linear(in_features=2048, out_features=1024, bias=False) (k_proj): Linear(in_features=2048, out_features=1024, bias=False) (v_proj): Linear(in_features=2048, out_features=2048, bias=False) (g_proj): Linear(in_features=2048, out_features=2048, bias=False) (gk_proj): Sequential( (0): Linear(in_features=2048, out_features=16, bias=False) (1): Linear(in_features=16, out_features=1024, bias=True) ) (o_proj): Linear(in_features=2048, out_features=2048, bias=False) (g_norm_swish_gate): FusedRMSNormGated(512, eps=1e-06, activation=swish) ) (mlp_norm): RMSNorm(2048, eps=1e-06) (mlp): GatedMLP( (gate_proj): Linear(in_features=2048, out_features=5632, bias=False) (up_proj): Linear(in_features=2048, out_features=5632, bias=False) (down_proj): Linear(in_features=5632, out_features=2048, bias=False) (swiglu_linear): SwiGLULinear() ) ) ) (norm): RMSNorm(2048, eps=1e-06) ) (lm_head): Linear(in_features=2048, out_features=32000, bias=False) ) ```
### Fused Modules We offer a collection of fused modules in `fla.modules` to facilitate faster training: * [`Rotary Embedding`](fla/modules/rotary.py): rotary positional embeddings as adopted by the Llama architecture, a.k.a., Transformer++. * [`Norm Layers`](fla/modules/layernorm.py): * `RMSNorm`, `LayerNorm` and `GroupNorm` * `RMSNormLinear`, `LayerNormLinear` and `GroupNormLinear` to reduce memory usage of intermediate tensors for improved memory efficiency. * [`Norm Layers with Gating`](fla/modules/fused_norm_gate.py): combine norm layers with element-wise sigmoid or swish gating, as used by RetNet/GLA. * [`Cross Entropy`](fla/modules/fused_cross_entropy.py): faster Triton implementation of cross entropy loss. * [`Linear Cross Entropy`](fla/modules/fused_linear_cross_entropy.py): fused linear layer and cross entropy loss to avoid the materialization of large logits tensors. Also refer to implementations by [mgmalek](https://github.com/mgmalek/efficient_cross_entropy) and [Liger-Kernel](https://github.com/linkedin/Liger-Kernel/blob/main/src/liger_kernel/ops/fused_linear_cross_entropy.py). * [`Linear KL Divergence`](fla/modules/fused_kl_div.py): fused linear layer and KL divergence loss in a similar vein as CE loss. > [!IMPORTANT] > You can control using `fuse_linear_cross_entropy` in the model configuration to enable/disable the fused linear cross entropy loss. > > This fused implementation is more memory-efficient but may reduce numerical precision. Due to this trade-off, it is disabled by default. > If you enable this feature and encounter training instability (e.g., loss divergence), we recommend disabling it to see if the issue is resolved. ### Generation Upon successfully pretraining a model, it becomes accessible for generating text using the 🤗 text generation APIs. In the following, we give a generation example: ```py >>> import fla >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> name = 'fla-hub/gla-1.3B-100B' >>> tokenizer = AutoTokenizer.from_pretrained(name) >>> model = AutoModelForCausalLM.from_pretrained(name).cuda() >>> input_prompt = "Power goes with permanence. Impermanence is impotence. And rotation is castration." >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.cuda() >>> outputs = model.generate(input_ids, max_length=64) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] ``` We also provide a simple script [here](benchmarks/benchmark_generation.py) for benchmarking the generation speed. Simply run it by: ```sh $ python -m benchmarks.benchmark_generation \ --path 'fla-hub/gla-1.3B-100B' \ --repetition_penalty 2. \ --prompt="Hello everyone, I'm Songlin Yang" Prompt: Hello everyone, I'm Songlin Yang Generated: Hello everyone, I'm Songlin Yang. I am a 20 year old girl from China who is currently studying in the United States of America for my Master degree and also working as an English teacher at school here on campus since last summer (1st semester). My main goal to be able do well with this course so that we can have Prompt length: 10, generation length: 64 Total prompt processing + decoding time: 4593ms ``` All of the pretrained models currently available can be found in [`fla-hub`](https://huggingface.co/fla-hub). ```py >>> from huggingface_hub import list_models >>> for model in list_models(author='fla-hub'): print(model.id) ``` ### Hybrid Models `fla` provides a flexible method to incorporate standard attention layers into existing linear attention models. This is easily achieved by specifying the `attn` argument in the model configuration. For example, to create a 2-layer Samba model with one Mamba layer followed by one local attention layer, using a sliding window size of 2048: ```py >>> from fla.models import SambaConfig >>> from transformers import AutoModelForCausalLM >>> config = SambaConfig(num_hidden_layers=2) >>> config.attn = { 'layers': [1], 'num_heads': 18, 'num_kv_heads': 18, 'qkv_bias': False, 'rope_theta': 10000., 'window_size': 2048 } >>> model = AutoModelForCausalLM.from_config(config) ```
Click to expand config and model structure ```py >>> config SambaConfig { "attn": { "layers": [ 1 ], "num_heads": 18, "num_kv_heads": 18, "qkv_bias": false, "rope_theta": 10000.0, "window_size": 2048 }, "bos_token_id": 1, "conv_kernel": 4, "eos_token_id": 2, "expand": 2, "fuse_cross_entropy": true, "fuse_norm": true, "fuse_swiglu": true, "hidden_act": "swish", "hidden_ratio": 4, "hidden_size": 2304, "initializer_range": 0.02, "intermediate_size": 4608, "max_position_embeddings": 2048, "model_type": "samba", "norm_eps": 1e-05, "num_hidden_layers": 2, "pad_token_id": 0, "rescale_prenorm_residual": false, "residual_in_fp32": false, "state_size": 16, "tie_word_embeddings": false, "time_step_floor": 0.0001, "time_step_init_scheme": "random", "time_step_max": 0.1, "time_step_min": 0.001, "time_step_rank": 144, "time_step_scale": 1.0, "transformers_version": "4.50.1", "use_bias": false, "use_cache": true, "use_conv_bias": true, "vocab_size": 32000 } >>> model SambaForCausalLM( (backbone): SambaModel( (embeddings): Embedding(32000, 2304) (layers): ModuleList( (0): SambaBlock( (mixer_norm): RMSNorm(2304, eps=1e-05) (mixer): Mamba( (conv1d): Conv1d(4608, 4608, kernel_size=(4,), stride=(1,), padding=(3,), groups=4608) (in_proj): Linear(in_features=2304, out_features=9216, bias=False) (x_proj): Linear(in_features=4608, out_features=176, bias=False) (dt_proj): Linear(in_features=144, out_features=4608, bias=True) (out_proj): Linear(in_features=4608, out_features=2304, bias=False) ) (mlp_norm): RMSNorm(2304, eps=1e-05) (mlp): GatedMLP( (gate_proj): Linear(in_features=2304, out_features=6144, bias=False) (up_proj): Linear(in_features=2304, out_features=6144, bias=False) (down_proj): Linear(in_features=6144, out_features=2304, bias=False) (swiglu_linear): SwiGLULinear() ) ) (1): SambaBlock( (mixer_norm): RMSNorm(2304, eps=1e-05) (mixer): Attention( (q_proj): Linear(in_features=2304, out_features=2304, bias=False) (k_proj): Linear(in_features=2304, out_features=2304, bias=False) (v_proj): Linear(in_features=2304, out_features=2304, bias=False) (o_proj): Linear(in_features=2304, out_features=2304, bias=False) (rotary): RotaryEmbedding(dim=128, base=10000.0, interleaved=False, pos_idx_in_fp32=True) ) (mlp_norm): RMSNorm(2304, eps=1e-05) (mlp): GatedMLP( (gate_proj): Linear(in_features=2304, out_features=6144, bias=False) (up_proj): Linear(in_features=2304, out_features=6144, bias=False) (down_proj): Linear(in_features=6144, out_features=2304, bias=False) (swiglu_linear): SwiGLULinear() ) ) ) (norm_f): RMSNorm(2304, eps=1e-05) ) (lm_head): Linear(in_features=2304, out_features=32000, bias=False) ) ```
During inference, you **DO NOT** need to revise anything for generation! The model will produce output as-is, without any need for additional configurations or modifications. ## Training We provide a minimal framework called [🔥 `flame`](https://github.com/fla-org/flame) built on top of `torchtitan`, for efficient training of `fla` models. Check out [the GLA example](https://github.com/fla-org/flash-linear-attention/blob/main/examples/training.md) for more details. ## Evaluation The [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library allows you to easily perform (zero-shot) model evaluations. Follow the steps below to use this library: 1. Install `lm_eval` following [their instructions](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/README.md). 2. Run evaluation with: ```sh $ MODEL='fla-hub/gla-1.3B-100B' $ python -m evals.harness --model hf \ --model_args pretrained=$MODEL,dtype=bfloat16 \ --tasks wikitext,lambada_openai,piqa,hellaswag,winogrande,arc_easy,arc_challenge,boolq,sciq,copa,openbookqa \ --batch_size 64 \ --num_fewshot 0 \ --device cuda \ --show_config ``` We've made `fla` compatible with hf-style evaluations, you can call [evals.harness](evals/harness.py) to finish the evaluations. Running the command above will provide the task results reported in the GLA paper. 3. Multi-GPU Evaluation with Hugging Face accelerate 🚀 To perform data-parallel evaluation (where each GPU loads a separate full copy of the model), we leverage the accelerate launcher as follows: ```sh $ MODEL='fla-hub/gla-1.3B-100B' $ accelerate launch -m evals.harness --model hf \ --model_args pretrained=$MODEL,dtype=bfloat16,trust_remote_code=True \ --tasks wikitext,lambada_openai,piqa,hellaswag,winogrande,arc_easy,arc_challenge,boolq,sciq,copa,openbookqa \ --batch_size 64 \ --num_fewshot 0 \ --device cuda \ --show_config \ --trust_remote_code ``` 4. 📏 RULER Benchmark suite The RULER benchmarks are commonly used for evaluating model performance on long-context tasks. You can evaluate `fla` models on RULER directly using `lm-evaluation-harness`. RULER is only available in a relatively recent version of `lm-evaluation-harness`, so make sure you have the latest version installed. ``` git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness cd lm-evaluation-harness pip install -e . ``` Then, install the necessary dependencies for RULER: ```sh pip install lm_eval["ruler"] ``` and run evaluation by (e.g., 32k contexts): ```sh $ accelerate launch -m evals.harness \ --output_path $OUTPUT \ --tasks niah_single_1,niah_single_2,niah_single_3,niah_multikey_1,niah_multikey_2,niah_multikey_3,niah_multiquery,niah_multivalue,ruler_vt,ruler_cwe,ruler_fwe,ruler_qa_hotpot,ruler_qa_squad \ --model_args pretrained=$MODEL,dtype=bfloat16,max_length=32768,trust_remote_code=True \ --metadata='{"max_seq_lengths":[4096,8192,16384,32768]}' \ --batch_size 2 \ --show_config \ --trust_remote_code ``` If a GPU can't load a full copy of the model, please refer to [this link](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#multi-gpu-evaluation-with-hugging-face-accelerate) for FSDP settings. > [!TIP] > If you are using `lm-evaluation-harness` as an external library and can't find (almost) any tasks available, before calling `lm_eval.evaluate()` or `lm_eval.simple_evaluate()`, simply run the following to load the library's stock tasks: > ```py > >>> from lm_eval.tasks import TaskManager; TaskManager().initialize_tasks() > ``` ## Benchmarks We compare our Triton-based implementations (`chunk_retention`, `chunk_gla`, `chunk_gdn`) with CUDA-based FlashAttention2 across various shape configurations. These tests were conducted on a single NVIDIA GB200 GPU (CUDA 12.9, PyTorch 2.9.0). ```sh # you might have to first install `fla` via `pip install -e .` to enable its import $ python -m benchmarks.ops.run --op chunk_retention chunk_gla chunk_gdn flash_attn ================================================================================= Machine: NVIDIA GB200 | CUDA 12.9 | PyTorch 2.9.0+cu129.msh ================================================================================= fwd B T H D op main[0a484709](ms) ----------------------------------------------------------------------- 1 8192 96 128 chunk_retention 0.787 chunk_gla 1.765 chunk_gdn 1.265 flash_attn 3.753 ----------------------------------------------------------------------- 2 16384 16 128 chunk_retention 0.792 chunk_gla 1.445 chunk_gdn 1.029 flash_attn 5.035 ----------------------------------------------------------------------- 4 2048 16 128 chunk_retention 0.559 chunk_gla 0.514 chunk_gdn 0.753 flash_attn 0.346 ----------------------------------------------------------------------- 4 4096 64 128 chunk_retention 0.997 chunk_gla 2.251 chunk_gdn 1.581 flash_attn 2.560 ----------------------------------------------------------------------- 8 1024 8 64 chunk_retention 0.425 chunk_gla 0.358 chunk_gdn 0.631 flash_attn 0.157 ----------------------------------------------------------------------- 8 2048 32 256 chunk_retention 1.174 chunk_gla 2.897 chunk_gdn 1.831 flash_attn 1.408 ================================================================================= fwdbwd B T H D op main[0a484709](ms) ----------------------------------------------------------------------- 1 8192 96 128 chunk_retention 2.618 chunk_gla 7.670 chunk_gdn 4.738 flash_attn 15.371 ----------------------------------------------------------------------- 2 16384 16 128 chunk_retention 2.122 chunk_gla 5.984 chunk_gdn 3.616 flash_attn 19.960 ----------------------------------------------------------------------- 4 2048 16 128 chunk_retention 1.047 chunk_gla 1.434 chunk_gdn 2.085 flash_attn 0.902 ----------------------------------------------------------------------- 4 4096 64 128 chunk_retention 3.459 chunk_gla 10.216 chunk_gdn 5.964 flash_attn 10.815 ----------------------------------------------------------------------- 8 1024 8 64 chunk_retention 0.898 chunk_gla 1.707 chunk_gdn 1.974 flash_attn 0.477 ----------------------------------------------------------------------- 8 2048 32 256 chunk_retention 51.103 chunk_gla 13.797 chunk_gdn 8.644 flash_attn 6.748 ================================================================================= ``` ## Citation If you find this repository helpful, please cite our work: ```bib @software{yang2024fla, title = {FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism}, author = {Yang, Songlin and Zhang, Yu}, url = {https://github.com/fla-org/flash-linear-attention}, month = jan, year = {2024} } @misc{zhang2025kda, title = {Kimi Linear: An Expressive, Efficient Attention Architecture}, author = {Zhang, Yu and Lin, Zongyu and Yao, Xingcheng and Hu, Jiaxi and Meng, Fanqing and Liu, Chengyin and Men, Xin and Yang, Songlin and Li, Zhiyuan and Li, Wentao and Lu, Enzhe and Liu, Weizhou and Chen, Yanru and Xu, Weixin and Yu, Longhui and Wang, Yejie and Fan, Yu and Zhong, Longguang and Yuan, Enming and Zhang, Dehao and Zhang, Yizhi and T. Liu, Y. and Wang, Haiming and Fang, Shengjun and He, Weiran and Liu, Shaowei and Li, Yiwei and Su, Jianlin and Qiu, Jiezhong and Pang, Bo and Yan, Junjie and Jiang, Zhejun and Huang, Weixiao and Yin, Bohong and You, Jiacheng and Wei, Chu and Wang, Zhengtao and Hong, Chao and Chen, Yutian and Chen, Guanduo and Wang, Yucheng and Zheng, Huabin and Wang, Feng and Liu, Yibo and Dong, Mengnan and Zhang, Zheng and Pan, Siyuan and Wu, Wenhao and Wu, Yuhao and Guan, Longyu and Tao, Jiawen and Fu, Guohong and Xu, Xinran and Wang, Yuzhi and Lai, Guokun and Wu, Yuxin and Zhou, Xinyu and Yang, Zhilin and Du, Yulun}, year = {2025}, eprint = {2510.26692}, archivePrefix = {arXiv}, primaryClass = {cs.CL} } @inproceedings{yang2025path, title = {PaTH Attention: Position Encoding via Accumulating Householder Transformations}, author = {Yang, Songlin and Shen, Yikang and Wen, Kaiyue and Tan, Shawn and Mishra, Mayank and Ren, Liliang and Panda, Rameswar and Kim, Yoon}, booktitle = {Proceedings of NeurIPS}, year = {2025} } @inproceedings{yang2024gdn, title = {Gated Delta Networks: Improving Mamba2 with Delta Rule}, author = {Yang, Songlin and Kautz, Jan and Hatamizadeh, Ali}, booktitle = {Proceedings of ICLR}, year = {2025} } @inproceedings{yang2024deltanet, title = {Parallelizing Linear Transformers with the Delta Rule over Sequence Length}, author = {Yang, Songlin and Wang, Bailin and Zhang, Yu and Shen, Yikang and Kim, Yoon}, booktitle = {Proceedings of NeurIPS}, year = {2024} } @inproceedings{zhang2024gsa, title = {Gated Slot Attention for Efficient Linear-Time Sequence Modeling}, author = {Zhang, Yu and Yang, Songlin and Zhu, Ruijie and Zhang, Yue and Cui, Leyang and Wang, Yiqiao and Wang, Bolun and Shi, Freda and Wang, Bailin and Bi, Wei and Zhou, Peng and Fu, Guohong}, booktitle = {Proceedings of NeurIPS}, year = {2024} } @inproceedings{qin2024hgrn2, title = {HGRN2: Gated Linear RNNs with State Expansion}, author = {Qin, Zhen and Yang, Songlin and Sun, Weixuan and Shen, Xuyang and Li, Dong and Sun, Weigao and Zhong, Yiran}, booktitle = {Proceedings of COLM}, year = {2024} } @inproceedings{yang2024gla, title = {Gated Linear Attention Transformers with Hardware-Efficient Training}, author = {Yang, Songlin and Wang, Bailin and Shen, Yikang and Panda, Rameswar and Kim, Yoon}, booktitle = {Proceedings of ICML}, year = {2024} } ``` ## Star History [![Stargazers repo roster for @fla-org/flash-linear-attention](https://bytecrank.com/nastyox/reporoster/php/stargazersSVG.php?user=fla-org&repo=flash-linear-attention)](https://github.com/fla-org/flash-linear-attention/stargazers) [![Star History Chart](https://api.star-history.com/svg?repos=fla-org/flash-linear-attention&type=Date)](https://star-history.com/#fla-org/flash-linear-attention&Date) ## Acknowledgements We extend our gratitude to [Bitdeer](https://www.bitdeer.com/) and [Moonshot AI](https://www.moonshot.ai/) for their support in maintaining and powering our project infrastructure.