vllm.models.deepseek_v41.amd.mono.attention.front

K1 front: one DeepSeek-V4.1 attention layer from its normed input row to the rotated query and the step's KV rows in the sliding-window cache.

One launch a layer and rank, BLOCKS CTAs x THREADS threads, S rows. Every CTA issues its weight loads (its wqkv part and its wq_b tasks) at the kernel start, before it waits on anything; the stages:

xq     (S > 16: S x 2 K halves, on the 32 CTAs without a wqkv part): the
       bf16 input's half -> vLLM's MXFP8 -> X8 (published, a flag each)
kt     (S): the token's attention keys for K2 -- top-k compressed slots
       through the kv source's block table, then window slots -> KT
wqkv_a (112 row tiles x 2 K halves, 16 rows): the input's MXFP8 half --
       quantized in the CTA (S <= 16) or X8's -- a token tile at a time
       (the next tile's rows in flight) times wqkv's -> fp32 partials ->
       QKVP
qkv    (S): the halves summed -> bf16; q_norm -> bf16 -> MXFP8 -> QX8
       (published, a flag a token); kv_norm -> bf16 -> GPT-J RoPE -> bf16
       -> the fp8_ds_mla record at the token's slot
wq_b   (H x 512 / 16, 16 rows): QX8 times wq_b -> bf16 -> GPT-J RoPE of
       each head's dims 448.. -> q (global, bf16)

Each elementwise step follows vLLM's ROCm path (mxfp8_quantize, FusedQKVRMSNorm, fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert); the GEMV reductions differ from the Triton GEMMs' only in their order. Adapted from ATOM's V4.1 attn_pre (K1), less its mHC seam.

Functions:

• await_flags –

  Thread u < n polls flag index(u); then the CTA proceeds.

• front_context –

  The front stages' context: LDS views, mailbox (tag tag), scratch

• front_lds –

  K1's LDS (the attention front's stages).

• publish –

  This CTA's stores visible device-wide, then the flag pair (region, i).

• quant_x_half –

  x_half_loads' values -> vLLM's MXFP8 rows of the K half in LDS (a

• run_front –

  The front's stages on this CTA. x8_given: a stage before this one

• stage_kt –

  Token t's keys for K2: KT[t][k] = slot | 1 << 31 for its top-k compressed

• stage_qkv –

  Token t: the K halves summed -> bf16; q_norm -> bf16 -> MXFP8 (QX8);

• stage_wqb –

  Rows 16 (task0 + BLOCKS k) .. of the rank's H x 512 query rows, every

• stage_wqkv –

  Rows 16 task .. of wqkv over K half part against tokens t0 .. t0 + n

• stage_xq –

  xq_loads' values -> vLLM's MXFP8 -> X8 words / X8S codes of the

• wqb_pos –

  This thread's token position for stage_wqb's epilogue of tile

• wqb_rope –

  (cos, sin) of this thread's pair in stage_wqb's epilogue over a tile

• x_half_loads –

  Tokens t0 .. t0 + n's bf16 input, K half part: its loads only (16 B a

• xq_loads –

  (token, K half) unit's bf16 input: its load only, 8 values a thread

await_flags(c, region, n, index)

Thread u < n polls flag index(u); then the CTA proceeds.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def await_flags(c, region, n, index):
    """Thread u < n polls flag ``index(u)``; then the CTA proceeds."""
    tid = c["tid"]
    if tid < n:
        c["mb"].poll([(region, index(fx.min(tid, n - 1)), 1)])
    gpu.barrier()

front_context(key, lds, args, scratch, tag, layout=None)

The front stages' context: LDS views, mailbox (tag tag), scratch regions, arguments (args: the kernel's, by name).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

def front_context(key, lds, args: dict, scratch, tag, layout=None) -> dict:
    """The front stages' context: LDS views, mailbox (tag ``tag``), scratch
    regions, arguments (``args``: the kernel's, by name)."""
    s, d = key.tokens, Dims(key.tp)
    tid = fx.thread_idx.x
    c = {
        "S": s,
        "tid": tid,
        "bid": fx.block_idx.x,
        "lane": tid % 64,
        "wave": tid // 64,
        "red": lds.red.ptr,
        "xl": lds.xl.ptr,
        "xsl": lds.xsl.ptr,
        "qxl": lds.qxl.ptr,
        "qxsl": lds.qxsl.ptr,
        "mb": Mailbox(tag),
        "d": d,
        "ratio": key.ratio,
        "ldbf": lambda ptr, i: fx.Float32(
            fx.BFloat16(bo.buffer_load(rsrc(ptr), i, vec_width=1, dtype=T.bf16))
        ),
        "args": args,
    }
    for region, (off, _) in (layout or front_scratch(s, d)).items():
        c[region] = scratch + fx.Int64(off)
    return c

front_lds(s, d)

K1's LDS (the attention front's stages).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

def front_lds(s: int, d: Dims):
    """K1's LDS (the attention front's stages)."""
    rows = tile_rows(s)
    wqb_per_cta = d.wqb_tasks // BLOCKS

    @fx.struct
    class FrontLds:
        red: fx.Array[fx.Float32, wqb_per_cta * RED + 16, 16]
        qxl: fx.Array[fx.Int32, rows * lds_row(QX_WORDS), 16]
        qxsl: fx.Array[fx.Int32, rows * lds_row(Q_RANK // 32), 16]
        xl: fx.Array[fx.Int32, rows * lds_row(KWORDS), 16]
        xsl: fx.Array[fx.Int32, rows * lds_row(KGROUPS), 16]

    return FrontLds

publish(c, region, i)

This CTA's stores visible device-wide, then the flag pair (region, i).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def publish(c, region, i):
    """This CTA's stores visible device-wide, then the flag pair (region, i)."""
    rocdl.s_waitcnt(vmcnt=0)
    gpu.barrier()
    if c["tid"] == 0:
        c["mb"].put(region, i, fx.Int32(1))

quant_x_half(c, regs, n)

x_half_loads' values -> vLLM's MXFP8 rows of the K half in LDS (a 32-group = 4 lanes).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def quant_x_half(c, regs, n):
    """``x_half_loads``' values -> vLLM's MXFP8 rows of the K half in LDS (a
    32-group = 4 lanes)."""
    tid, lane = c["tid"], c["lane"]
    x_row, s_row = lds_row(KWORDS), lds_row(KGROUPS)
    hr0, col = tile_map(tid, XH_COLS)
    act = tid < XH_RPR * XH_COLS
    for i in range_constexpr(len(regs)):
        hr = hr0 + XH_RPR * i
        w = regs[i]
        vals = []
        for d in range_constexpr(4):
            vals += [bf_lo(w[d]), bf_hi(w[d])]
        amax = abs(vals[0])
        for v in vals[1:]:
            amax = fx.max(amax, abs(v))
        amax = butterfly(amax, (1, 2), fx.max)
        code = mx_code(amax)
        mul = mx_mul(code)
        w0 = fp8_pack4(*[clamp_fp8(vals[q] * mul) for q in range(4)])
        w1 = fp8_pack4(*[clamp_fp8(vals[4 + q] * mul) for q in range(4)])
        tok = hr >> 1
        half = hr & 1
        if act & (hr < 2 * n):
            fx.ptr_store(
                fx.Vector.from_elements([w0, w1], fx.Int32),
                c["xl"] + (tok * x_row + half * (KWORDS // 2) + col * 2),
            )
            if lane % 4 == 0:
                fx.ptr_store(
                    code, c["xsl"] + (tok * s_row + half * (KGROUPS // 2) + (col >> 2))
                )
    gpu.barrier()

run_front(c, key, bid, x8_given=False, before_wqkv=None, after_wqkv=None)

The front's stages on this CTA. x8_given: a stage before this one (the mono layer's seam norm) publishes X8 / XRDY for every token (no input quantization here). before_wqkv() runs ahead of the wqkv weights' loads (a latency-critical stage of the caller's), after_wqkv() right after.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def run_front(c, key, bid, x8_given=False, before_wqkv=None, after_wqkv=None):
    """The front's stages on this CTA. ``x8_given``: a stage before this one
    (the mono layer's seam norm) publishes X8 / XRDY for every token (no input
    quantization here). ``before_wqkv()`` runs ahead of the wqkv weights' loads
    (a latency-critical stage of the caller's), ``after_wqkv()`` right after."""
    s = key.tokens
    wqb_tasks = Dims(key.tp).wqb_tasks
    a = c["args"]
    tid = c["tid"]
    tiles = token_tiles(s)
    wqb_per_cta = wqb_tasks // BLOCKS
    assert wqb_tasks % BLOCKS == 0
    n_wqkv = WQKV_TASKS * WQKV_KPARTS  # 224
    assert n_wqkv <= BLOCKS
    n_free = BLOCKS - n_wqkv  # CTAs without a wqkv part: xq, kt
    QKV0 = 0  # on wqkv CTAs
    WQB0 = 0
    in_cta_quant = s <= TILE and not x8_given
    xq_stage = s > TILE and not x8_given
    xq_units = WQKV_KPARTS * s
    xq_per = (xq_units + n_free - 1) // n_free
    stamp(c, 0)

    # ---- Loads complete in issue order, so every CTA issues its latency-
    # critical loads before its later stages' weights: a wqkv CTA its first
    # input tile and its wqkv part, the next tiles while it quantizes, then
    # its wq_b tasks; a CTA with a key-table task those after that work.
    # The q / kv norms run on wqkv CTAs, their partials' wait behind the
    # wq_b weights that are due by then anyway.
    wqkv_unit = first_task(bid, 0)
    has_wqkv = wqkv_unit < n_wqkv
    task = fx.min(wqkv_unit, n_wqkv - 1) % WQKV_TASKS
    part = fx.min(wqkv_unit, n_wqkv - 1) // WQKV_TASKS
    wqb_task = first_task(bid, WQB0)
    free = bid - n_wqkv
    is_free = free >= 0
    if const_expr(in_cta_quant):
        xregs = [x_half_loads(c, part, has_wqkv, *tiles[0])]
    if const_expr(xq_stage):
        xunits = [free + n_free * i for i in range(xq_per)]
        xlive = [is_free & (u < xq_units) for u in xunits]
        xq_regs = [
            xq_loads(c, fx.min(fx.max(u, 0), xq_units - 1), lv)
            for u, lv in zip(xunits, xlive)
        ]
    if const_expr(before_wqkv is not None):
        before_wqkv()
    kops = gemv_loads(
        c,
        a["wqkv"],
        a["wqkv_s"],
        HIDDEN,
        task * ROWS,
        kbeg=part * KSTEPS,
        nsteps=KSTEPS,
        pred=has_wqkv,
    )
    if const_expr(after_wqkv is not None):
        after_wqkv()
    if const_expr(xq_stage):  # noqa: SIM102 (a build-time and a run-time test)
        if is_free:
            for i in range_constexpr(xq_per):
                stage_xq(
                    c, fx.min(fx.max(xunits[i], 0), xq_units - 1), xq_regs[i], xlive[i]
                )
            rocdl.s_waitcnt(vmcnt=0)
            gpu.barrier()
            if tid < xq_per:
                u = free + n_free * tid
                if u < xq_units:
                    c["mb"].put(c["xrdy"], u, fx.Int32(1))
    for t in range(is_free.select(free, fx.Int32(s)), s, n_free):
        stage_kt(c, t)
    stamp(c, 1)
    poss = [wqb_pos(c, t0, n) for t0, n in tiles]

    # ---- wqkv_a: one (row tile, K half) a CTA, every token tile; the wq_b
    # weights issued behind the input rows' loads
    def wqb_weights():
        return [
            gemv_loads(c, a["wqb"], a["wqb_s"], Q_RANK, (wqb_task + BLOCKS * i) * ROWS)
            for i in range(wqb_per_cta)
        ]

    if const_expr(in_cta_quant):
        if has_wqkv:
            quant_x_half(c, xregs[0], tiles[0][1])
        bops = wqb_weights()
        if has_wqkv:
            stage_wqkv(c, task, part, kops, *tiles[0], has_wqkv)
    else:
        await_flags(c, c["xrdy"], s, lambda u: WQKV_KPARTS * u + part)
        xr = (
            c["x8"],
            c["x8s"],
            X_WORDS,
            part * KWORDS,
            KWORDS,
            X_GROUPS,
            part * KGROUPS,
            KGROUPS,
        )
        regs = rows_loads(c, *xr, tiles[0][0], tiles[0][1], pred=has_wqkv)
        if const_expr(len(tiles) == 1):
            bops = wqb_weights()
        for ti in range_constexpr(len(tiles)):
            t0, n = tiles[ti]
            rows_store(c, regs, KWORDS, KGROUPS, c["xl"], c["xsl"], n)
            if const_expr(ti + 1 < len(tiles)):
                t1, n1 = tiles[ti + 1]
                regs = rows_loads(c, *xr, t1, n1, pred=has_wqkv)
            if const_expr(ti + 2 == len(tiles)):
                bops = wqb_weights()
            stage_wqkv(c, task, part, kops, t0, n, has_wqkv)
    stamp(c, 2)

    # ---- q / kv norms, KV insert
    for t in range(first_task(bid, QKV0), s, BLOCKS):
        stage_qkv(c, t)
    stamp(c, 3)

    # ---- wq_b: every task's rows of a token tile at a time, the next
    # tile's rows in flight
    ropes = [wqb_rope(c, wqb_task, poss[ti], tiles[ti][1]) for ti in range(len(tiles))]
    await_flags(c, c["qrdy"], s, lambda u: u)
    qx = (c["qx8"], c["qx8s"], QX_WORDS, 0, QX_WORDS, Q_RANK // 32, 0, Q_RANK // 32)
    regs = rows_loads(c, *qx, *tiles[0])
    for ti in range_constexpr(len(tiles)):
        t0, n = tiles[ti]
        rows_store(c, regs, QX_WORDS, Q_RANK // 32, c["qxl"], c["qxsl"], n)
        if const_expr(ti + 1 < len(tiles)):
            regs = rows_loads(c, *qx, *tiles[ti + 1])
        stage_wqb(c, wqb_task, bops, t0, n, ropes[ti])
    stamp(c, 4)

stage_kt(c, t)

Token t's keys for K2: KT[t][k] = slot | 1 << 31 for its top-k compressed rows (k < ntopk), the window slot for k in [ntopk, kv_len), -1 for an invalid or absent key; KLEN[t] = kv_len (0 for a pad row), KLEN[S + t] = ntopk.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def stage_kt(c, t):
    """Token t's keys for K2: ``KT[t][k] = slot | 1 << 31`` for its top-k compressed
    rows (k < ntopk), the window slot for k in [ntopk, kv_len), -1 for an
    invalid or absent key; KLEN[t] = kv_len (0 for a pad row), KLEN[S + t] =
    ntopk."""
    tid = c["tid"]
    a, ratio = c["args"], c["ratio"]
    slot = ld_i32(a["slot"], 2 * t)
    live = slot >= 0
    nswa = live.select(ld_i32(a["swa_lens"], t), fx.Int32(0))
    if const_expr(ratio > 0):
        pos = ld_i32(a["pos"], 2 * t)
        ntopk = live.select(fx.min((pos + 1) // ratio, fx.Int32(TOPK)), fx.Int32(0))
        req = ld_i32(a["t2r"], t)
    else:
        ntopk = fx.Int32(0)
    kv_len = ntopk + nswa
    for i in range_constexpr((KEYS + THREADS - 1) // THREADS):
        k = fresh(tid) + THREADS * i
        sidx = fx.min(fx.max(k - ntopk, 0), WINDOW - 1)
        s_slot = ld_i32(a["swa_idx"], t * WINDOW + sidx)
        val = (k < kv_len).select(s_slot, fx.Int32(-1))
        if const_expr(ratio > 0):
            local = ld_i32(a["topk"], t * TOPK + fx.min(k, TOPK - 1))
            lc = fx.max(local, 0)
            e = a["comp_block"]
            blk = ld_i32(a["comp_bt"], req * a["bt_stride"] + lc // e)
            c_slot = (blk * e + lc % e) | fx.Int32(-2147483648)
            c_val = (local >= 0).select(c_slot, fx.Int32(-1))
            val = (k < ntopk).select(c_val, val)
        if k < KEYS:
            bo.buffer_store(val, rsrc(a["kt"]), t * KEYS + k)
    if tid == 0:
        bo.buffer_store(kv_len, rsrc(a["klen"]), t)
        bo.buffer_store(ntopk, rsrc(a["klen"]), c["S"] + t)

stage_qkv(c, t)

Token t: the K halves summed -> bf16; q_norm -> bf16 -> MXFP8 (QX8); kv_norm -> bf16 -> RoPE -> bf16 -> the fp8_ds_mla record at slot_mapping[t]. 256 threads (8 q columns and 2 kv columns each); threads 256.. mirror thread 255 and store nothing.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def stage_qkv(c, t):
    """Token t: the K halves summed -> bf16; q_norm -> bf16 -> MXFP8 (QX8);
    kv_norm -> bf16 -> RoPE -> bf16 -> the fp8_ds_mla record at
    slot_mapping[t]. 256 threads (8 q columns and 2 kv columns each); threads
    256.. mirror thread 255 and store nothing."""
    tid, lane, wave, red = c["tid"], c["lane"], c["wave"], c["red"]
    a, s = c["args"], c["S"]
    tt = fx.min(tid, 255)
    mine = tid < 256
    qc = [fx.min(tt * 8 + j, Q_RANK - 1) for j in range(8)]
    # independent loads first: they overlap the partials' wait
    qw = [c["ldbf"](a["qn_w"], qc[j]) for j in range(8)]
    kw = [c["ldbf"](a["kvn_w"], tt * 2 + j) for j in range(2)]
    pos = ld_i32(a["pos"], 2 * t)
    slot = ld_i32(a["slot"], 2 * t)
    jr = tt - NOPE // 2
    is_rope = jr >= 0
    jj = fx.max(jr, 0)
    cs = is_rope.select(ld_f32(a["cos_sin"], pos * ROPE + jj), fx.Float32(1.0))
    sn = is_rope.select(ld_f32(a["cos_sin"], pos * ROPE + HALF + jj), fx.Float32(0.0))
    specs = []
    for p in range_constexpr(2):
        specs += [(c["qkvp"], (p * s + t) * QKV_ROWS + qc[j], 1) for j in range(8)]
        specs += [
            (c["qkvp"], (p * s + t) * QKV_ROWS + Q_RANK + tt * 2 + j, 1)
            for j in range(2)
        ]
    got = c["mb"].poll(specs)
    vals = [
        bf16_round(got[j][0].bitcast(fx.Float32) + got[10 + j][0].bitcast(fx.Float32))
        for j in range(10)
    ]
    xq = [((tt * 8 + j) < Q_RANK).select(vals[j], fx.Float32(0.0)) for j in range(8)]
    xk = vals[8:10]
    ssq = xq[0] * xq[0]
    for j in range_constexpr(1, 8):
        ssq = ssq + xq[j] * xq[j]
    ssq = butterfly(ssq, (32, 16, 8, 4, 2, 1))
    ssk = butterfly(xk[0] * xk[0] + xk[1] * xk[1], (32, 16, 8, 4, 2, 1))
    if (lane == 0) & mine:
        fx.ptr_store(ssq, red + wave)
        fx.ptr_store(ssk, red + 4 + wave)
    gpu.barrier()
    tq = (fx.ptr_load(red + 0) + fx.ptr_load(red + 1)) + (
        fx.ptr_load(red + 2) + fx.ptr_load(red + 3)
    )
    tk = (fx.ptr_load(red + 4) + fx.ptr_load(red + 5)) + (
        fx.ptr_load(red + 6) + fx.ptr_load(red + 7)
    )
    rq = hw_rsq(
        div_rn(tq, fx.Float32(float(Q_RANK)), fx.Float32(1.0 / Q_RANK))
        + fx.Float32(EPS)
    )
    rk = hw_rsq(
        div_rn(tk, fx.Float32(float(KV_DIM)), fx.Float32(1.0 / KV_DIM))
        + fx.Float32(EPS)
    )
    # q: bf16 normed row, then vLLM's MXFP8 (a 32-group = 4 threads)
    ys = [bf16_round((xq[j] * rq) * qw[j]) for j in range(8)]
    amax = abs(ys[0])
    for y in ys[1:]:
        amax = fx.max(amax, abs(y))
    code = mx_code(butterfly(amax, (1, 2), fx.max))
    mul = mx_mul(code)
    if mine & (tt * 8 < Q_RANK):
        w0 = fp8_pack4(*[clamp_fp8(ys[i] * mul) for i in range(4)])
        w1 = fp8_pack4(*[clamp_fp8(ys[4 + i] * mul) for i in range(4)])
        bo.buffer_store(
            fx.Vector.from_elements([w0, w1], fx.Int32),
            rsrc(c["qx8"]),
            t * QX_WORDS + 2 * tt,
            cache_modifier=CM_DEV,
        )
        if tt % 4 == 0:
            bo.buffer_store(
                code,
                rsrc(c["qx8s"]),
                t * (Q_RANK // 32) + tt // 4,
                cache_modifier=CM_DEV,
            )
    # kv: this thread's pair (2 tt, 2 tt + 1)
    kn = [bf16_round((xk[j] * rk) * kw[j]) for j in range(2)]
    e0 = bf16_round(kn[0] * cs - kn[1] * sn)
    e1 = bf16_round(kn[0] * sn + kn[1] * cs)
    # record quant of the nope dims: a 64-block = 32 threads (half a wave)
    amax = butterfly(fx.max(abs(e0), abs(e1)), (1, 2, 4, 8, 16), fx.max)
    rcode = ceil_exp(
        div_rn(
            fx.max(amax, fx.Float32(1e-4)), fx.Float32(448.0), fx.Float32(1.0 / 448.0)
        )
    )
    inv = mx_mul(rcode)
    q0 = clamp_fp8(e0 * inv)
    q1 = clamp_fp8(e1 * inv)
    n0 = xshfl(q0, 1)
    n1 = xshfl(q1, 1)
    word = fp8_pack4(q0, q1, n0, n1)
    rope_word = (
        fx.Vector.from_elements([e0, e1], fx.Float32)
        .to(fx.BFloat16)
        .bitcast(fx.Int32)[0]
    )
    blk = a["swa_block"]
    sblock = slot // blk
    soff = slot % blk
    base = a["swa"] + fx.Int64(sblock) * fx.Int64(a["swa_stride"])
    rec = base + fx.Int64(soff * DATA)
    if mine & (tt % 32 == 0) & (tt < NOPE // 2):
        fx.ptr_store(rcode.bitcast(fx.Float32), red + 8 + tt // 32)
    gpu.barrier()
    if mine & (slot >= 0):
        if tt < NOPE // 2:
            if tt % 2 == 0:
                gstore(rec + fx.Int64(2 * tt), word, words=1)
        else:
            gstore(rec + fx.Int64(NOPE + 4 * (tt - NOPE // 2)), rope_word, words=1)
        if tt == 0:
            sb = [fx.ptr_load(red + 8 + b).bitcast(fx.Int32) for b in range(7)]
            w0 = sb[0] | (sb[1] << 8) | (sb[2] << 16) | (sb[3] << 24)
            w1 = sb[4] | (sb[5] << 8) | (sb[6] << 16)
            sc = base + fx.Int64(blk * DATA + soff * 8)
            gstore(sc, fx.Vector.from_elements([w0, w1], fx.Int32), words=2)
    publish(c, c["qrdy"], t)

stage_wqb(c, task0, bops, t0, n, rope)

Rows 16 (task0 + BLOCKS k) .. of the rank's H x 512 query rows, every task k of the CTA: GEMVs -> bf16 -> GPT-J RoPE of a head's dims 448.. (fp32) -> bf16 -> q.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def stage_wqb(c, task0, bops, t0, n, rope):
    """Rows 16 (task0 + BLOCKS k) .. of the rank's H x 512 query rows, every
    task k of the CTA: GEMVs -> bf16 -> GPT-J RoPE of a head's dims 448..
    (fp32) -> bf16 -> q."""
    tid, red, d = c["tid"], c["red"], c["d"]
    a = c["args"]
    nt = len(bops)
    for k in range_constexpr(nt):
        gemv_mfmas(c, Q_RANK, c["qxl"], c["qxsl"], red + k * RED, bops[k], rows=n)
    gpu.barrier()
    per = ROWS * n // 2  # bf16 pairs a task
    if tid < nt * per:
        k = tid // per
        u = tid % per
        rp = u % (ROWS // 2)
        tl = u // (ROWS // 2)
        t = plus(t0, tl)
        r0 = (task0 + BLOCKS * k) * ROWS + 2 * rp
        rk = red + k * RED
        v0 = bf16_round(row_sum(rk, 2 * rp, tl))
        v1 = bf16_round(row_sum(rk, 2 * rp + 1, tl))
        is_rope = (task0 * ROWS) % HEAD_DIM + 2 * rp >= NOPE
        cs, sn = rope
        e0 = is_rope.select(v0 * cs - v1 * sn, v0)
        e1 = is_rope.select(v0 * sn + v1 * cs, v1)
        word = (
            fx.Vector.from_elements([e0, e1], fx.Float32)
            .to(fx.BFloat16)
            .bitcast(fx.Int32)[0]
        )
        bo.buffer_store(word, rsrc(a["q"]), (t * d.q_rows + r0) // 2)
    gpu.barrier()

stage_wqkv(c, task, part, ops, t0, n, live)

Rows 16 task .. of wqkv over K half part against tokens t0 .. t0 + n -> fp32 partials QKVP[part] (live: a CTA with a wqkv part).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def stage_wqkv(c, task, part, ops, t0, n, live):
    """Rows 16 task .. of wqkv over K half ``part`` against tokens t0 .. t0 + n
    -> fp32 partials QKVP[part] (``live``: a CTA with a wqkv part)."""
    tid, red = c["tid"], c["red"]
    gemv_mfmas(c, HIDDEN, c["xl"], c["xsl"], red, ops, rows=n, nsteps=KSTEPS)
    gpu.barrier()
    if (tid < ROWS * n) & live:
        r = tid % ROWS
        tl = tid // ROWS
        t = plus(t0, tl)
        v = row_sum(red, r, tl)
        c["mb"].put(c["qkvp"], (part * c["S"] + t) * QKV_ROWS + task * ROWS + r, v)
    gpu.barrier()

stage_xq(c, unit, w, live)

xq_loads' values -> vLLM's MXFP8 -> X8 words / X8S codes of the unit (published by the caller).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

@traced
def stage_xq(c, unit, w, live):
    """``xq_loads``' values -> vLLM's MXFP8 -> X8 words / X8S codes of the
    unit (published by the caller)."""
    tid, lane = c["tid"], c["lane"]
    vals = []
    for d in range_constexpr(4):
        vals += [bf_lo(w[d]), bf_hi(w[d])]
    amax = abs(vals[0])
    for v in vals[1:]:
        amax = fx.max(amax, abs(v))
    amax = butterfly(amax, (1, 2), fx.max)
    code = mx_code(amax)
    mul = mx_mul(code)
    w0 = fp8_pack4(*[clamp_fp8(vals[q] * mul) for q in range(4)])
    w1 = fp8_pack4(*[clamp_fp8(vals[4 + q] * mul) for q in range(4)])
    t = unit >> 1
    part = unit & 1
    if live & (tid < XQ_ITEMS):
        bo.buffer_store(
            fx.Vector.from_elements([w0, w1], fx.Int32),
            rsrc(c["x8"]),
            t * X_WORDS + part * KWORDS + tid * 2,
            cache_modifier=CM_DEV,
        )
        if lane % 4 == 0:
            bo.buffer_store(
                code,
                rsrc(c["x8s"]),
                t * X_GROUPS + part * KGROUPS + (tid >> 2),
                cache_modifier=CM_DEV,
            )

wqb_pos(c, t0, n)

This thread's token position for stage_wqb's epilogue of tile t0 .. t0 + n.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

def wqb_pos(c, t0, n):
    """This thread's token position for ``stage_wqb``'s epilogue of tile
    t0 .. t0 + n."""
    per = ROWS * n // 2
    tl = fx.min((c["tid"] % per) // (ROWS // 2), n - 1)
    return ld_i32(c["args"]["pos"], 2 * (t0 + tl))

wqb_rope(c, task0, pos_t, n)

(cos, sin) of this thread's pair in stage_wqb's epilogue over a tile of n rows (pos_t: its token's position; every task of the CTA shares the pair's dims: tasks BLOCKS apart are whole heads apart).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

def wqb_rope(c, task0, pos_t, n):
    """(cos, sin) of this thread's pair in ``stage_wqb``'s epilogue over a tile
    of n rows (``pos_t``: its token's position; every task of the CTA shares
    the pair's dims: tasks BLOCKS apart are whole heads apart)."""
    a = c["args"]
    rp = c["tid"] % (ROWS // 2)
    dim = (task0 * ROWS) % HEAD_DIM + 2 * rp
    jj = fx.max((dim - NOPE) >> 1, 0)
    return ld_f32(a["cos_sin"], pos_t * ROPE + jj), ld_f32(
        a["cos_sin"], pos_t * ROPE + HALF + jj
    )

x_half_loads(c, part, pred, t0, n)

Tokens t0 .. t0 + n's bf16 input, K half part: its loads only (16 B a lane-item, 8 values; a thread one column of [2 n half-rows, 160]), masked off on CTAs without a wqkv part.

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

def x_half_loads(c, part, pred, t0, n):
    """Tokens t0 .. t0 + n's bf16 input, K half ``part``: its loads only (16 B a
    lane-item, 8 values; a thread one column of [2 n half-rows, 160]), masked
    off on CTAs without a wqkv part."""
    a = c["args"]
    xr = rsrc(a["x"], pred.select(fx.Int64(0xFFFFFFFF), fx.Int64(0)))
    hr0, col = tile_map(c["tid"], XH_COLS)
    e0 = part * XH + col * 8
    out = []
    for i in range((2 * n + XH_RPR - 1) // XH_RPR):
        hr = fx.min(hr0 + XH_RPR * i, 2 * n - 1)
        e = (t0 + (hr >> 1)) * a["x_stride"] + (hr & 1) * (XH // 2) + e0
        out.append(fx.Vector(bo.buffer_load(xr, e >> 1, vec_width=4, dtype=T.i32)))
    return out

xq_loads(c, unit, pred)

(token, K half) unit's bf16 input: its load only, 8 values a thread (threads past the half mirror its last item).

Source code in vllm/models/deepseek_v41/amd/mono/attention/front.py

def xq_loads(c, unit, pred):
    """(token, K half) ``unit``'s bf16 input: its load only, 8 values a thread
    (threads past the half mirror its last item)."""
    a = c["args"]
    xr = rsrc(a["x"], pred.select(fx.Int64(0xFFFFFFFF), fx.Int64(0)))
    col = fx.min(c["tid"], XQ_ITEMS - 1)
    e = (unit >> 1) * a["x_stride"] + (unit & 1) * XH + col * 8
    return fx.Vector(bo.buffer_load(xr, e >> 1, vec_width=4, dtype=T.i32))