fla/ops/gated_oja_rule/fused_recurrent.py

# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang


import torch
import triton
import triton.language as tl

from fla.ops.utils.op import exp
from fla.utils import input_guard


@triton.heuristics({
    'USE_GV': lambda args: args['gv'] is not None,
    'USE_INITIAL_STATE': lambda args: args['h0'] is not None,
    'STORE_FINAL_STATE': lambda args: args['ht'] is not None,
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None
})
@triton.jit(do_not_specialize=['T'])
def fused_recurrent_oja_fwd_kernel(
    q,
    k,
    v,
    gv,
    beta,
    o,
    h0,
    ht,
    cu_seqlens,
    scale,
    T,
    B: tl.constexpr,
    H: tl.constexpr,
    HV: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    USE_GV: tl.constexpr,
    USE_Q_L2NORM: tl.constexpr,
    USE_K_L2NORM: tl.constexpr,
    IS_BETA_HEADWISE: tl.constexpr,
    USE_INITIAL_STATE: tl.constexpr,
    STORE_FINAL_STATE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_v, i_nh = tl.program_id(0), tl.program_id(1)
    i_n, i_hv = i_nh // HV, i_nh % HV
    i_h = i_hv // (HV // H)
    if IS_VARLEN:
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
        T = eos - bos
    else:
        bos, eos = i_n * T, i_n * T + T
    o_k = tl.arange(0, BK)
    o_v = i_v * BV + tl.arange(0, BV)

    p_q = q + (bos * H + i_h) * K + o_k
    p_k = k + (bos * H + i_h) * K + o_k
    p_v = v + (bos * HV + i_hv) * V + o_v
    if USE_GV:
        p_gv = gv + (bos * HV + i_hv) * V + o_v
    if IS_BETA_HEADWISE:
        p_beta = beta + bos * HV + i_hv
    else:
        p_beta = beta + (bos * HV + i_hv) * V + o_v

    p_o = o + (bos * HV + i_hv) * V + o_v

    mask_k = o_k < K
    mask_v = o_v < V
    mask_h = mask_k[:, None] & mask_v[None, :]

    b_h = tl.zeros([BK, BV], dtype=tl.float32)
    if USE_INITIAL_STATE:
        p_h0 = h0 + i_nh * K*V + o_k[:, None] * V + o_v[None, :]
        b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)

    for _ in range(0, T):
        b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32)
        b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32)
        b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32)
        if USE_Q_L2NORM:
            b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6)
        if USE_K_L2NORM:
            b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6)
        b_q = b_q * scale
        if IS_BETA_HEADWISE:
            b_beta = tl.load(p_beta).to(tl.float32)
        else:
            b_beta = tl.load(p_beta, mask=mask_v, other=0).to(tl.float32)

        # [BK, BV]
        if USE_GV:
            b_gv = tl.load(p_gv, mask=mask_v, other=0).to(tl.float32)
            b_h *= exp(b_gv[None, :])

        b_k = b_beta * (b_k - tl.sum(b_h * b_v[None, :], 1))
        b_h += b_k[:, None] * b_v

        # [BV]
        b_o = tl.sum(b_h * b_q[:, None], 0)
        tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v)

        p_q += H*K
        p_k += H*K
        p_v += HV*V
        if USE_GV:
            p_gv += HV*V
        p_beta += HV * (1 if IS_BETA_HEADWISE else V)
        p_o += HV*V

    if STORE_FINAL_STATE:
        p_ht = ht + i_nh * K*V + o_k[:, None] * V + o_v[None, :]
        tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)


def fused_recurrent_oja_fwd(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    gv: torch.Tensor | None = None,
    beta: torch.Tensor | None = None,
    scale: float = None,
    initial_state: torch.Tensor = None,
    output_final_state: bool = False,
    use_q_l2norm: bool = False,
    use_k_l2norm: bool = False,
    cu_seqlens: torch.LongTensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    B, T, H, K, V = *k.shape, v.shape[-1]
    assert V <= 128
    HV = v.shape[2]
    N = B if cu_seqlens is None else len(cu_seqlens) - 1
    BK, BV = triton.next_power_of_2(K), min(triton.next_power_of_2(V), 256)
    NV = triton.cdiv(V, BV)
    num_stages = 3
    num_warps = 1

    o = torch.empty_like(v)
    final_state = q.new_empty(N, HV, K, V, dtype=torch.float32) if output_final_state else None

    grid = (NV, N * HV)
    fused_recurrent_oja_fwd_kernel[grid](
        q=q,
        k=k,
        v=v,
        gv=gv,
        beta=beta,
        o=o,
        h0=initial_state,
        ht=final_state,
        cu_seqlens=cu_seqlens,
        scale=scale,
        T=T,
        B=B,
        H=H,
        HV=HV,
        K=K,
        V=V,
        BK=BK,
        BV=BV,
        IS_BETA_HEADWISE=beta.ndim != v.ndim,
        USE_Q_L2NORM=use_q_l2norm,
        USE_K_L2NORM=use_k_l2norm,
        num_warps=num_warps,
        num_stages=num_stages,
    )
    return o, final_state


class FusedRecurrentFunction(torch.autograd.Function):

    @staticmethod
    @input_guard
    def forward(
        ctx,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        gv: torch.Tensor | None = None,
        beta: torch.Tensor | None = None,
        scale: float = None,
        initial_state: torch.Tensor = None,
        output_final_state: bool = False,
        use_q_l2norm: bool = False,
        use_k_l2norm: bool = False,
        cu_seqlens: torch.LongTensor | None = None,
    ):
        o, final_state = fused_recurrent_oja_fwd(
            q=q,
            k=k,
            v=v,
            gv=gv,
            beta=beta,
            scale=scale,
            initial_state=initial_state,
            output_final_state=output_final_state,
            use_q_l2norm=use_q_l2norm,
            use_k_l2norm=use_k_l2norm,
            cu_seqlens=cu_seqlens,
        )

        return o, final_state

    @staticmethod
    @input_guard
    def backward(ctx, do, dht):
        raise NotImplementedError(
            "Backward pass is not implemented yet and we do not have plans to implement it "
            "because we haven't figured out how to compute dg without materializing the full "
            "hidden states for all time steps."
        )


def fused_recurrent_gated_oja_rule(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    gv: torch.Tensor | None = None,
    beta: torch.Tensor | None = None,
    scale: float = None,
    initial_state: torch.Tensor = None,
    output_final_state: bool = False,
    use_q_l2norm: bool = False,
    use_k_l2norm: bool = False,
    cu_seqlens: torch.LongTensor | None = None,
    **kwargs,
) -> tuple[torch.Tensor, torch.Tensor]:

    if 'use_qk_l2norm_in_kernel' in kwargs and (not use_q_l2norm and not use_k_l2norm):
        use_q_l2norm = True
        use_k_l2norm = True

    if cu_seqlens is not None:
        if q.shape[0] != 1:
            raise ValueError(
                f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`."
                f"Please flatten variable-length inputs before processing."
            )
        if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1:
            raise ValueError(
                f"The number of initial states is expected to be equal to the number of input sequences, "
                f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}."
            )
    if scale is None:
        scale = k.shape[-1] ** -0.5
    if beta is None:
        beta = torch.ones_like(q[..., 0])

    o, final_state = FusedRecurrentFunction.apply(
        q,
        k,
        v,
        gv,
        beta,
        scale,
        initial_state,
        output_final_state,
        use_q_l2norm,
        use_k_l2norm,
        cu_seqlens,
    )
    return o, final_state