fla/ops/path_attn/parallel_path_bwd_inter_dqh.py

import torch
import triton
import triton.language as tl

from fla.ops.utils import prepare_chunk_indices, prepare_chunk_offsets
from fla.ops.utils.op import exp2
from fla.utils import check_shared_mem


@triton.heuristics({
    'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
    'USE_GATE': lambda args: args['g_cumsum'] is not None,
})
@triton.jit(do_not_specialize=['T'])
def parallel_path_bwd_dq_kernel(
    q,
    k,
    v,
    g_cumsum,
    hc_whole,
    scale,
    L,
    D,
    dq,
    do,
    dhc_whole,
    dg_cumsum,
    cu_seqlens,
    indices,
    split_offsets,  # varlen specific
    T,
    G: tl.constexpr,
    HQ: tl.constexpr,
    H: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BS: tl.constexpr,
    BK: tl.constexpr,
    BV: tl.constexpr,
    S: tl.constexpr,  # aka larger chunk size
    NUM_BLOCKS: tl.constexpr,
    IS_VARLEN: tl.constexpr,
    USE_GATE: tl.constexpr,
):
    i_t, i_nh = tl.program_id(0), tl.program_id(1)
    i_n, i_hq = i_nh // HQ, i_nh % HQ
    i_h = i_hq // G

    if IS_VARLEN:
        i_n, i_t = tl.load(indices + i_t * 2).to(tl.int32), tl.load(indices + i_t * 2 + 1).to(tl.int32)
        boh_large = tl.load(split_offsets + i_n).to(tl.int32)
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
        T = eos - bos
    else:
        bos, eos = i_n * T, i_n * T + T
        boh_large = i_n * tl.cdiv(T, S)
    o_t = i_t * BT + tl.arange(0, BT)
    m_t = o_t < T

    k += (bos * H + i_h) * K  # GQA when H!=HQ
    v += (bos * H + i_h) * V  # GQA when H!=HQ
    do += (bos * HQ + i_hq) * V
    dq += (bos * HQ + i_hq) * K
    hc_whole += (boh_large * H + i_h) * K * K
    dhc_whole += (boh_large * HQ + i_hq) * K * K
    L += (bos * HQ + i_hq)
    D += (bos * HQ + i_hq)
    if USE_GATE:
        g_cumsum += (bos * HQ + i_hq)
        dg_cumsum += (bos * HQ + i_hq)

    # constants
    stride_h = H * K * K
    stride_hq = HQ * K * K
    sm_scale = scale * 1.44269504

    # load query
    p_do = tl.make_block_ptr(do, (T, V), (HQ*V, 1), (i_t * BT, 0), (BT, BV), (1, 0))
    b_do = tl.load(p_do, boundary_check=(0, 1))

    p_l = tl.make_block_ptr(L, (T,), (HQ,), (i_t * BT,), (BT,), (0,))
    p_d = tl.make_block_ptr(D, (T,), (HQ,), (i_t * BT,), (BT,), (0,))
    b_l = tl.load(p_l, boundary_check=(0,))
    b_delta = tl.load(p_d, boundary_check=(0,))

    if USE_GATE:
        p_g_cumsum_q = tl.make_block_ptr(g_cumsum, (T,), (HQ,), (i_t * BT,), (BT,), (0,))
        b_g_cumsum_q = tl.load(p_g_cumsum_q, boundary_check=(0,)).to(tl.float32)
        b_dg_cumsum_q = tl.zeros([BT], dtype=tl.float32)
    else:
        b_g_cumsum_q = None
        b_dg_cumsum_q = None

    curr_end = ((i_t * BT // S) * S).to(tl.int32)
    b_dq = tl.zeros([BT, K], dtype=tl.float32)

    for offset_outer in range(0, curr_end, S):
        idx_j = offset_outer // S
        p_q = tl.make_block_ptr(q + ((bos.to(tl.int64) * NUM_BLOCKS + idx_j + 1) * HQ + i_hq) * K, (T, K),
                                (HQ*K*NUM_BLOCKS, 1), (i_t * BT, 0), (BT, BK), (1, 0))
        b_q = tl.load(p_q, boundary_check=(0, 1))

        b_dh = -tl.dot(tl.trans(b_q), b_dq.to(b_q.dtype))
        tl.atomic_add(dhc_whole + idx_j * stride_hq + tl.arange(0, K)
                      [:, None] * K + tl.arange(0, K)[None, :], b_dh, sem='relaxed')
        p_h = tl.make_block_ptr(hc_whole + idx_j * stride_h, (K, K), (K, 1), (0, 0), (BK, BK), (1, 0))
        b_h = tl.load(p_h, boundary_check=(0, 1))
        b_dq = b_dq - tl.dot(b_dq.to(b_h.dtype), tl.trans(b_h))

        for offset in range(offset_outer, min(offset_outer+S, i_t*BT), BS):
            p_k = tl.make_block_ptr(k, (T, K), (H * K, 1), (offset, 0), (BS, BK), (1, 0))
            b_k = tl.load(p_k, boundary_check=(0, 1))
            b_A = tl.dot(b_q, tl.trans(b_k).to(b_q.dtype))
            if USE_GATE:
                p_g_cumsum_k = tl.make_block_ptr(g_cumsum, (T,), (HQ,), (offset,), (BS,), (0,))
                b_g_cumsum_k = tl.load(p_g_cumsum_k, boundary_check=(0,)).to(tl.float32)
                b_A = b_A + b_g_cumsum_q[:, None] - b_g_cumsum_k[None, :]
            b_A = exp2(b_A * sm_scale - b_l[:, None])
            b_A = tl.where(m_t[:, None], b_A, 0)
            p_v = tl.make_block_ptr(v, (T, V), (H*V, 1), (offset, 0), (BS, BV), (1, 0))
            b_v = tl.load(p_v, boundary_check=(0, 1))
            b_dp = tl.dot(b_do, tl.trans(b_v).to(b_do.dtype))
            b_dA = (b_dp - b_delta[:, None]) * b_A * scale
            b_dq += tl.dot(b_dA.to(b_k.dtype), b_k)
            if USE_GATE:
                b_dg_cumsum_q += tl.sum(b_dA, axis=1)

    p_dq = tl.make_block_ptr(dq, (T, K), (K * HQ, 1), (i_t * BT, 0), (BT, BK), (1, 0))
    tl.store(p_dq, b_dq.to(dq.dtype.element_ty), boundary_check=(0, 1))
    if USE_GATE:
        tl.atomic_add(dg_cumsum + o_t * HQ, b_dg_cumsum_q, mask=m_t, sem='relaxed')


def parallel_path_bwd_dq_fn(
    q,
    k,
    v,
    g_cumsum,
    do,
    dg_cumsum,
    hc_whole,
    scale,
    L,
    D,
    cu_seqlens,
    S,
    BT,
    BS,
    chunk_indices: torch.LongTensor | None = None,
):
    B, T, num_blocks, HQ, K = q.shape
    H, V = v.shape[-2:]
    G = HQ // H
    BK, BV = triton.next_power_of_2(K), triton.next_power_of_2(V)

    if chunk_indices is None and cu_seqlens is not None:
        chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
    indices = chunk_indices
    split_offsets = prepare_chunk_offsets(cu_seqlens, S) if cu_seqlens is not None else None
    NT = triton.cdiv(T, BT) if cu_seqlens is None else len(indices)

    # should be NS
    if cu_seqlens is not None:
        assert split_offsets[-1] == hc_whole.shape[0]
    dq = torch.empty(B, T, HQ, K, dtype=torch.float32, device=q.device)

    # [NS, HQ, K, K] instead of [NS, H, K, K]
    # atomic add must be initialized to 0
    dhc_whole = torch.zeros(hc_whole.shape[0], HQ, K, K, dtype=torch.float32, device=q.device)

    parallel_path_bwd_dq_kernel[(NT, B*HQ)](
        q=q,
        k=k,
        v=v,
        g_cumsum=g_cumsum,
        hc_whole=hc_whole,
        scale=scale,
        L=L,
        D=D,
        dq=dq,
        do=do,
        dhc_whole=dhc_whole,
        dg_cumsum=dg_cumsum,
        cu_seqlens=cu_seqlens,
        indices=indices,
        split_offsets=split_offsets,
        T=T,
        S=S,
        BT=BT,
        BS=BS,
        G=G,
        HQ=HQ,
        H=H,
        K=K,
        V=V,
        BK=BK,
        BV=BV,
        NUM_BLOCKS=num_blocks,
        num_warps=8 if (BT == 128 and K == 128) else 4,
        num_stages=3 if check_shared_mem('ampere') else 2,
    )
    return dq, dhc_whole, dg_cumsum