tools/factory/specs/comba_forward.py

"""Spec for `comba-forward` — Comba (2025), a delta-rule variant with a decoupled read key."""
import pathlib
import sys

sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1]))
from spec import TaskSpec

SPEC = TaskSpec(
    name="comba-forward",
    title="Write a fast Comba forward kernel",
    blurb=("Comba is a 2025 linear-attention layer: the gated delta rule with a DECOUPLED read key — the "
           "state correction is read with a separate vector p while the write still uses k, so the "
           "within-chunk transform is no longer the symmetric one DeltaNet's WY trick relies on."),
    keywords=["mle", "kernel-generation", "comba", "linear-attention", "delta-rule", "gpu"],
    module="comba.py",
    func="comba_forward",
    signature="comba_forward(q, k, v, p, beta, g, scale=None)",
    returns_doc="""Comba forward.

Args:
    q, k, p: (B, T, H, K) bfloat16 — queries, write-keys, read-keys.
    v:       (B, T, H, V) bfloat16 — values.
    beta:    (B, T, H)    bfloat16 — delta-rule step size in (0, 1).
    g:       (B, T, H)    float32  — per-step log-decay (<= 0); exp(g) is the gate.
    scale: float or None — query scale; None means K ** -0.5.

Returns:
    o: (B, T, H, V), bfloat16 or float32 — must match /app/reference.py numerically.""",

    reference_imports="import torch",
    reference_src='''
def comba_forward(q, k, v, p, beta, g, scale=None):
    """Comba forward, written as the plain step-by-step recurrence in fp32.

    Correct and simple — it is the numerical SPECIFICATION, not a performance target.
    """
    q, k, v, p, beta, g = [x.transpose(1, 2).contiguous().to(torch.float32) for x in (q, k, v, p, beta, g)]
    B, H, T, K = k.shape
    V = v.shape[-1]
    if scale is None:
        scale = K ** -0.5
    q = q * scale

    o = torch.zeros(B, H, T, V, device=q.device, dtype=torch.float32)
    h = torch.zeros(B, H, K, V, device=q.device, dtype=torch.float32)
    for i in range(T):
        h = h * g[:, :, i].exp()[..., None, None]                       # scalar forget gate
        v_i = v[:, :, i] - (h * p[:, :, i][..., None]).sum(-2)          # read the state with p
        v_i = v_i * beta[:, :, i][..., None]                            # delta-rule step size
        h = h + k[:, :, i].unsqueeze(-1) * v_i.unsqueeze(-2)            # write the state with k
        o[:, :, i] = torch.einsum('bhd,bhdm->bhm', q[:, :, i], h)       # readout
    return o.transpose(1, 2).contiguous()
''',
    make_inputs_src='''
def _mk(B, T, H, K, V, seed):
    import torch.nn.functional as F
    gen = torch.Generator(device="cuda").manual_seed(seed)
    q = torch.randn(B, T, H, K, device="cuda", dtype=torch.bfloat16, generator=gen)
    k = F.normalize(torch.randn(B, T, H, K, device="cuda", generator=gen), dim=-1).to(torch.bfloat16)
    v = torch.randn(B, T, H, V, device="cuda", dtype=torch.bfloat16, generator=gen)
    p = F.normalize(torch.randn(B, T, H, K, device="cuda", generator=gen), dim=-1).to(torch.bfloat16)
    beta = torch.rand(B, T, H, device="cuda", generator=gen).sigmoid().to(torch.bfloat16)
    g = (F.logsigmoid(torch.randn(B, T, H, device="cuda", generator=gen)) / 4.0).to(torch.float32)
    return q, k, v, p, beta, g
''',
    flops_src='''
def canonical_work(B, T, H, K, V, C=64):
    """FLOPs attributed to one Comba forward, from the SHAPE ALONE (chunked form, chunk length C)."""
    return B * H * T * (2 * C * (2 * K + V) + 6 * K * V)
''',

    metric="TFLOP/s",
    compare="tensor",
    tol=2e-2,
    shape_names=("B", "T", "H", "K", "V"),
    grader_shapes=[(2, 8192, 32, 128, 128), (4, 4096, 32, 128, 128), (2, 16384, 32, 128, 128),
                   (4, 8192, 16, 128, 128), (2, 8192, 32, 64, 128)],
    measure_shapes=[(3, 6144, 32, 128, 128), (2, 12288, 24, 128, 128), (4, 8192, 32, 128, 128),
                    (2, 8192, 16, 128, 128), (4, 4096, 16, 64, 128)],
    measure_quick_shapes=[(1, 2048, 16, 128, 128), (2, 2048, 8, 128, 128), (1, 4096, 16, 64, 128)],
    correct_shapes=[(1, 256, 4, 128, 128), (2, 512, 8, 64, 128), (1, 512, 6, 128, 128),
                    (2, 128, 4, 64, 64)],

    spec_md="""Per batch `b` and head `h`, with a recurrent state `S` of shape `(K, V)` initialised to zero,
for `t = 0 … T-1`:

```
S   = exp(g_t) * S                       # scalar forget gate on the whole state
u_t = beta_t * ( v_t - S^T p_t )         # correction, read with p (NOT with k)
S   = S + k_t u_t^T                      # rank-1 write, using k
o_t = S^T (scale * q_t)                  # readout
```

`scale` defaults to `K ** -0.5`.

The single thing that distinguishes Comba from a gated delta rule is that the state is **read with `p` and
written with `k`**. In DeltaNet those are the same vector, which is what makes its within-chunk transform
`(I - tril(beta k kᵀ))⁻¹` symmetric-ish and lets the WY/UT trick apply directly. Here the corresponding
chunk matrix is built from `p` against `k` and is **not** symmetric, so the standard transform has to be
re-derived rather than reused.

`/app/reference.py` writes the recurrence out step by step in fp32. That is the exact specification; it is
deliberately simple rather than fast, and its runtime has no bearing on your score.""",

    contract_md="""| arg | shape | dtype | meaning |
|-----|-------|-------|---------|
| `q` | `(B, T, H, K)` | `bfloat16` | queries |
| `k` | `(B, T, H, K)` | `bfloat16` | **write** keys (L2-normalised along `K`) |
| `v` | `(B, T, H, V)` | `bfloat16` | values |
| `p` | `(B, T, H, K)` | `bfloat16` | **read** keys (L2-normalised along `K`) |
| `beta` | `(B, T, H)` | `bfloat16` | delta-rule step size, in `(0, 1)` |
| `g` | `(B, T, H)` | `float32` | per-step log-decay, `<= 0` (`exp(g)` is the gate) |
| `scale` | scalar | `float` or `None` | query scale; `None` means `K ** -0.5` |

**Return** `o` of shape `(B, T, H, V)`, dtype `bfloat16` or `float32`.

Note the argument order is `(q, k, v, p, beta, g)` — `p` comes after `v`, and `g` is last.

All tensors are CUDA and contiguous. `T` is a multiple of 64. No initial/final state, no variable-length
packing, no GQA. Treat all inputs as read-only.""",

    regime_md="""**Shape regime you are graded in** (the exact grader sizes are *not* disclosed): `B` in 2–4,
`T` in 4096–16384, `H` in 16–32, `K` in {64, 128}, `V` = 128. These are large enough that kernel time, not
launch overhead, dominates. Write a **general** kernel — one tuned to a single shape will not score well.""",

    correctness_md="""Your output must match the reference (evaluated in fp32 as a stable ground truth)
within **relative Frobenius error `2e-2`** at every graded shape, including the timed ones.""",

    perf_md="""The reference walks the sequence one position at a time, so essentially all of its time is
launch overhead on tiny operations — but note that beating it is trivial and **not** the point: your score is
absolute throughput, so the question is how close to the machine's roofline you get.

The real work is the chunked reformulation. Within a chunk of length `C`, the `C` rank-1 writes can be
linearised into a single transform so the chunk becomes dense matmuls, and only a small state has to be
carried sequentially between chunks. Because the read key `p` differs from the write key `k`, the chunk
matrix here is `tril(beta ⊙ (p kᵀ))` rather than the symmetric DeltaNet form — derive the inverse (a
forward substitution) and fuse it into the same kernel rather than materialising it. Keep the `(K, V)` state
in registers/shared memory across chunks, use bf16 tensor cores for the chunk matmuls with fp32
accumulation, and fold the scalar decay into the matmul operands instead of materialising `exp(g)` tensors.""",

    precision_md="""All inputs and outputs are **bfloat16** — this is an LLM kernel and that is the precision
it runs at in production. Your kernel is expected to do its matmuls on **bf16 tensor cores with fp32
accumulation**. **fp8** is acceptable anywhere you can still hold the tolerance.

For calibration, a correct bf16 fused delta-rule-family kernel lands around **4e-3** relative error against
the fp32 recurrence — roughly 5x inside the `2e-2` gate — while a wrong algorithm misses by 0.2 or more. The
state carry is the sensitive part: accumulate it in fp32, since errors there compound along the sequence and
will grow with `T`.

Do **not** infer from the reference that fp32 compute is wanted. It runs in fp32 purely to be a stable
numerical *specification*.""",
).validate()