---
title: Attention
description: Supported attention modules in Axolotl
---

Axolotl routes attention via a single config field:

```yaml
attn_implementation: <backend>
```

`attn_implementation` is passed through to `transformers` verbatim (via
`model.config._attn_implementation`). Accepted values are the HF-native
backends, axolotl-registered backends, or a hub-kernel path.

## Backends

| `attn_implementation` | Description |
|---|---|
| `eager` | Plain PyTorch attention. No packing support. |
| `sdpa` | PyTorch `scaled_dot_product_attention`. No packing support. |
| `flash_attention_2` | Dao-AILab Flash Attention 2. |
| `flash_attention_3` | Dao-AILab Flash Attention 3 (Hopper+). |
| `flex_attention` | Torch Flex Attention (requires torch ≥ 2.6). |
| `xformers` | xFormers memory-efficient attention. |
| `sage` | SageAttention (QK int8 / PV fp16). |
| `s2` | Shifted-Sparse Attention (LLaMA only, FA2 under the hood). |
| `fp8` | torchao FP8 low-precision attention (requires SM90+, torch ≥ 2.11). Loaded as SDPA and patched post-load. |
| `kernels-community/flash-attn3` | HF hub FA3 kernel. |
| `kernels-community/sage-attention` | HF hub SageAttention kernel. |
| Other `<org>/<name>` path | Any hub-kernel path supported by `transformers`. |

Short-form aliases (`flash`, `fa2`, `flex`, `sdp`, etc.) are **not accepted** —
set the canonical name above.

### Capability flags

Axolotl derives three boolean capability flags from `attn_implementation` and
exposes them on the validated config:

- `cfg.attn_supports_packing` — backend supports varlen sample packing via
  `position_ids`. Gates multipack patches and `sample_packing_drop_attention_mask`.
- `cfg.attn_uses_flash_lib` — backend needs the `flash_attn` (Dao-AILab)
  monkeypatches (FA4 auto, LLaMA flash hijack, ring-FA).
- `cfg.attn_needs_dtype_cast` — backend requires fp16/bf16 embeddings
  (everything except `eager` and `sdpa`).

These are **computed** — they cannot be overridden from YAML.

## Per-backend notes

### SDPA

Default PyTorch attention. See
[PyTorch docs](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html).

```yaml
attn_implementation: sdpa
```

### Flash Attention

Axolotl supports FA2, FA3, and FA4. The best available version is used
automatically based on your installed packages and GPU.

```yaml
attn_implementation: flash_attention_2  # or flash_attention_3
```

#### Flash Attention 2

Requirements: Ampere, Ada, or Hopper GPUs (Turing or lower not supported)

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

::: {.callout-tip}

If you get `undefined symbol` while training, ensure you installed PyTorch prior to Axolotl.
Alternatively, try reinstall or downgrade a version.

:::

#### Flash Attention 3

Requirements: Hopper only and CUDA 12.8 (recommended)

```bash
git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention/hopper
python setup.py install
```

#### Flash Attention 4

Requirements: Hopper or Blackwell GPUs. Auto-applied when `attn_uses_flash_lib`
is true and FA4 is importable.

```bash
pip install flash-attn-4
```

Or from source:

```bash
git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention/flash_attn/cute
pip install -e .

# FA2's flash_attn package includes a cute/ stub that shadows FA4.
# Remove it so Python can find the real FA4 module:
rm -r $(python -c "import flash_attn; print(flash_attn.__path__[0])")/cute
```

::: {.callout-note}

**Hopper (SM90) users**: The backward kernel is not yet included in the pip package. To use FA4
for training on Hopper, install from source using the instructions above.

:::

::: {.callout-warning}

FA4 only supports head dimensions up to 128 (`d ≤ 128`). The DeepSeek shape `(192, 128)` is
also supported but only on Blackwell. Axolotl automatically detects incompatible head dimensions
and falls back to FA2/3.

:::

### AMD

Requirements: ROCm 6.0 and above. See
[Flash Attention AMD docs](https://github.com/Dao-AILab/flash-attention/tree/main?tab=readme-ov-file#amd-rocm-support).

### Flex Attention

```yaml
attn_implementation: flex_attention
torch_compile: true  # recommended
```

Requires torch ≥ 2.6. See [PyTorch docs](https://pytorch.org/blog/flexattention/).

### SageAttention

Requirements: Ampere, Ada, or Hopper GPUs.

```yaml
attn_implementation: sage
```

```bash
pip install sageattention==2.2.0 --no-build-isolation
```

::: {.callout-warning}

Only LoRA/QLoRA recommended. Full finetuning has been observed to drop loss to 0. See
[GitHub Issue](https://github.com/thu-ml/SageAttention/issues/198).

:::

For more details: [Sage Attention](https://github.com/thu-ml/SageAttention).

### xFormers

```yaml
attn_implementation: xformers
```

::: {.callout-tip}

Recommended for Turing GPUs or below (e.g. Colab T4).

:::

### Shifted Sparse Attention

::: {.callout-warning}

Planned for deprecation. Prefer one of the backends above.

:::

Requirements: LLaMA model architecture. Loaded as FA2 under the hood and
patched to implement shifted-sparse attention. Does not support sample packing.

```yaml
attn_implementation: s2
```

### FP8

torchao low-precision attention. Loaded as SDPA and patched post-load.

Requirements: SM90+ (Hopper/Blackwell), PyTorch ≥ 2.11, torchao ≥ 0.17,
flash-attn with FA3. KV caching must be disabled.

```yaml
attn_implementation: fp8
```

### Hub kernels

```yaml
attn_implementation: kernels-community/flash-attn3
```

Passed through to `transformers`; axolotl does not install the kernel itself.
For recognized hub paths the capability flags are set automatically; for
arbitrary paths axolotl uses conservative defaults (`attn_supports_packing=False`,
`attn_uses_flash_lib=False`).

## Migrating from legacy boolean flags

The following legacy config fields are **deprecated** and will be removed in a
future release. Each emits a `DeprecationWarning` when set and is stripped from
the validated config.

| Legacy | Canonical |
|---|---|
| `flash_attention: true` | `attn_implementation: flash_attention_2` |
| `sdp_attention: true` | `attn_implementation: sdpa` |
| `xformers_attention: true` | `attn_implementation: xformers` |
| `flex_attention: true` | `attn_implementation: flex_attention` |
| `sage_attention: true` | `attn_implementation: sage` |
| `s2_attention: true` | `attn_implementation: s2` |
| `eager_attention: true` | `attn_implementation: eager` |

Combining `attn_implementation` with a legacy flag (e.g. `attn_implementation:
flash_attention_2` **and** `flash_attention: true`) raises — pick one.

::: {.callout-note}

Existing example configs under `examples/` still use the legacy flags. They
continue to work with a deprecation warning; they will be migrated in a
follow-up pass.

:::