---
license: mit
tags:
  - kernel
  - kernels
  - triton
  - attention
  - long-context
---

# Hydra

Hydra is an experimental bounded-residency attention kernel for long-context
decode. It keeps sink tokens, recent tokens, and selected older pages resident
instead of forcing each decode step to attend over the full KV cache.

Source code: [https://github.com/newjordan/hydra/tree/main/hf-kernels/hydra](https://github.com/newjordan/hydra/tree/main/hf-kernels/hydra)

This release is intentionally narrow. It is not a general replacement for
full attention, and it does not claim universal speedups or broad quality
preservation. The current target is fit and usability for specific
long-context inference workloads where the full-attention path is memory-bound.

## Usage

After the kernel is published:

```python
import torch
from kernels import get_kernel

hydra = get_kernel("Frosty40/hydra")

q = torch.randn(1, 32, 1, 128, device="cuda", dtype=torch.bfloat16)
k = torch.randn(1, 8, 8192, 128, device="cuda", dtype=torch.bfloat16)
v = torch.randn(1, 8, 8192, 128, device="cuda", dtype=torch.bfloat16)

out = hydra.hydra(q, k, v)
print(out.shape)
```

For local development from the public source checkout:

```python
from pathlib import Path
import sys

sys.path.insert(0, str(Path("hf-kernels") / "hydra" / "torch-ext"))
import hydra
```

`readme_example.py` uses the local source packet by default so it can run before
publication. Set `HYDRA_USE_HUB=1` after publication to exercise the Hub-loaded
path.

## API

```python
hydra.hydra(
    q,
    k,
    v,
    *,
    is_causal=True,
    sliding_window=None,
    policy_layer_idx=None,
    precision="high",
)
```

Current constraints:

- CUDA tensors only
- bf16 `q`, `k`, and `v`
- shape `(B, H, T, D)` with `D=128`
- causal attention only
- decode path supports `Tq == 1` with arbitrary `Tkv`
- prefill path requires `T % BLOCK_SIZE == 0`

## Evidence Boundary

Submission-facing evidence must come from checked artifacts, not prose notes.
Treat evidence in three separate scopes:

- kernel/package validation: tests, CUDA parity logs, `kernel-builder` logs, and
  isolated decode benchmarks for this source packet
- broad Hydra research campaign: capacity, quality, sparse-attention comparison,
  edge/OOM, diagnostic, and model-family reports from the staging repo
- exact-model proof-of-concept: checked `Qwen/Qwen3.6-35B-A3B-FP8` rows for
  named GPUs only

The exact-Qwen proof-of-concept appendix in the staging repo is under:

```text
results/raw/qwen3p6_35b_a3b_fp8/
results/reports/QWEN3P6_FP8_EVIDENCE_TABLE.md
```

Each cited row must include all three:

- fit/headroom: GPU, context length, memory allocated/reserved, and OOM state
- quality/correctness: prompt/task ID and generated answer artifact
- speed/usability: wall time, generated tokens, tokens/sec, and comparison target

Do not cite proxy models, loader-only probes, failed dependency checks, or
non-matching model runs as Hydra benchmark results. Do not describe the
exact-Qwen proof-of-concept subset as the full Hydra validation campaign.

## Current Proof-Of-Concept Scope

The current exact-Qwen artifact-backed proof-of-concept scope is:

| GPU | Model | Scope |
| --- | --- | --- |
| RTX PRO 6000 WS | `Qwen/Qwen3.6-35B-A3B-FP8` | 32k/80k/160k repeat packet, 160k c96 warm packet, and frontier/headroom sweeps |
| RTX 3090 | `Qwen/Qwen3.6-35B-A3B-FP8` | 2k/3k/4k/6k/8k fit probes and completed 10k/12k/14k edge sweep |

The 3090 result should be framed as fit/usability evidence, not a speedup
claim. Token rates are slow in the long-context edge rows. The broader Hydra
campaign includes additional GPUs, tasks, and comparison lanes outside this
exact-model appendix.

## Validation Required Before Merge

Minimum gates for source changes:

```bash
cd hf-kernels/hydra
python3 -m pytest -q tests
nix run .#ci-test
python3 benchmarks/benchmark_hydra_decode.py --repo .
python3 readme_example.py
```

Run the CUDA tests on real GPUs. Local syntax checks are not enough for a
kernel submission.

## Benchmark Snapshot

The current 8192-token decode smoke/benchmark matrix is intentionally reported
as kernel/package evidence, not as a universal speedup claim.

| GPU | Package smoke decode | HF benchmark mean |
| --- | ---: | ---: |
| RTX 3060 | 0.2574 ms | 0.3229 ms |
| RTX 3070 | 0.1474 ms | 0.2532 ms |
| RTX 3080 | 0.2051 ms | 0.3157 ms |
| RTX 3090 | 0.1492 ms | 0.3107 ms |
| RTX 4070 Ti | 0.1261 ms | 0.2215 ms |
| RTX 4090 | 0.1132 ms | 0.2245 ms |
| A100 SXM4 | 0.1408 ms | 0.2568 ms |
| RTX PRO 6000 Blackwell | 0.1158 ms | 0.1371 ms |
| RTX A6000 | builder smoke 0.2166 ms | 0.3230 ms |

The final `kernel-builder` gate passed on a Vast RTX A6000 with
`BUILDER_VARIANT=torch210-cxx11-cu128-x86_64-linux`: local pytest `6 passed`,
decode smoke `0.2166 ms/iter`, builder pytest `4 passed, 2 skipped`, exit
status `0`.