submission 727033

#!POPCORN leaderboard amd-mixed-mla
#!POPCORN gpu MI355X
"""Hybrid: HIP FP4 unfused (kv<=2048) + aiter FP8 (kv>2048)"""
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import os, lzma, base64, ctypes, struct, torch
from task import input_t, output_t

NQ = 16; QKD = 576; VD = 512; SM_SCALE = 1.0 / (QKD ** 0.5)

# =========================================================================
# HIP FP4 kernels (for kv <= 2048)
# =========================================================================
_hip = ctypes.CDLL("libamdhip64.so")
_hip.hipModuleLoadData.restype = ctypes.c_int
_hip.hipModuleLoadData.argtypes = [ctypes.c_void_p, ctypes.c_void_p]
_hip.hipModuleGetFunction.restype = ctypes.c_int
_hip.hipModuleGetFunction.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
_hip.hipModuleLaunchKernel.restype = ctypes.c_int
_hip.hipModuleLaunchKernel.argtypes = [
    ctypes.c_void_p, ctypes.c_uint, ctypes.c_uint, ctypes.c_uint,
    ctypes.c_uint, ctypes.c_uint, ctypes.c_uint,
    ctypes.c_uint, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]

_co_data = lzma.decompress(base64.b64decode(_CO_LZ_B64))
_co_buf = (ctypes.c_char * len(_co_data)).from_buffer_copy(_co_data)
_mod = ctypes.c_void_p()
_hip.hipModuleLoadData(ctypes.byref(_mod), _co_buf)

def _get_fn(mod, name):
    fn = ctypes.c_void_p()
    r = _hip.hipModuleGetFunction(ctypes.byref(fn), mod, name)
    assert r == 0, f"hipModuleGetFunction({name}) = {r}"
    return fn

_fn_qk = _get_fn(_mod, b"_Z16fp4_gemm_batchedPK12hip_bfloat16PKhS3_PS_iiiiiiii")
_fn_sm = _get_fn(_mod, b"_Z18softmax_fp4_kernelPK12hip_bfloat16PhS2_fii")
_fn_pv = _get_fn(_mod, b"_Z19fp4_pv_fp4a_batchedPKhS0_S0_S0_P12hip_bfloat16iiiiiiiiii")

SMEM_QK = 54784
SMEM_PV = 21760 + 64

def _make_cfg(args_bytes):
    ab = (ctypes.c_char * len(args_bytes)).from_buffer(args_bytes)
    sz = ctypes.c_size_t(len(args_bytes))
    return (ctypes.c_void_p * 5)(
        ctypes.c_void_p(1), ctypes.cast(ab, ctypes.c_void_p),
        ctypes.c_void_p(2), ctypes.cast(ctypes.pointer(sz), ctypes.c_void_p),
        ctypes.c_void_p(3))

def _launch_qk(A_ptr, B_ptr, Bs_ptr, C_ptr, M, N, K, bs, sA, sB, sBs, sc_per_tok, sC):
    BM, BN = 16, 64
    grid_x = ((M + BM - 1) // BM) * ((N + BN - 1) // BN)
    args = bytearray(64)
    struct.pack_into("Q", args, 0, A_ptr)
    struct.pack_into("Q", args, 8, B_ptr)
    struct.pack_into("Q", args, 16, Bs_ptr)
    struct.pack_into("Q", args, 24, C_ptr)
    for i, v in enumerate([M, N, K, sA, sB, sBs, sc_per_tok, sC]):
        struct.pack_into("i", args, 32 + i*4, v)
    cfg = _make_cfg(args)
    _hip.hipModuleLaunchKernel(_fn_qk, grid_x, bs, 1, 256, 1, 1, SMEM_QK,
        ctypes.c_void_p(0), ctypes.c_void_p(0), cfg)

def _launch_sm(scores_ptr, pfp4_ptr, psc_ptr, sm_scale, bs, nq, kv):
    args = bytearray(40)
    struct.pack_into("Q", args, 0, scores_ptr)
    struct.pack_into("Q", args, 8, pfp4_ptr)
    struct.pack_into("Q", args, 16, psc_ptr)
    struct.pack_into("f", args, 24, sm_scale)
    struct.pack_into("i", args, 28, nq)
    struct.pack_into("i", args, 32, kv)
    cfg = _make_cfg(args)
    _hip.hipModuleLaunchKernel(_fn_sm, bs * nq, 1, 1, 256, 1, 1, 0,
        ctypes.c_void_p(0), ctypes.c_void_p(0), cfg)

def _launch_pv(afp4, asc, vfp4, vsc, c, M, N, K, bs, sAfp4, sAsc, sVfp4, fp4s, sVsc, scs, sC):
    BM, BN = 16, 64
    grid_x = ((M + BM - 1) // BM) * ((N + BN - 1) // BN)
    args = bytearray(96)
    for i, v in enumerate([afp4, asc, vfp4, vsc, c]):
        struct.pack_into("Q", args, i*8, v)
    for i, v in enumerate([M, N, K, sAfp4, sAsc, sVfp4, fp4s, sVsc, scs, sC]):
        struct.pack_into("i", args, 40 + i*4, v)
    cfg = _make_cfg(args)
    _hip.hipModuleLaunchKernel(_fn_pv, grid_x, bs, 1, 256, 1, 1, SMEM_PV,
        ctypes.c_void_p(0), ctypes.c_void_p(0), cfg)

_hip_cache = {}

def _hip_fp4_kernel(data):
    q_bf16, kv_data, qo_indptr, kv_indptr, config = data
    bs = config["batch_size"]; kv = config["kv_seq_len"]
    kv_fp4, kv_sc = kv_data["mxfp4"]
    Kfp4 = kv_fp4.view(torch.uint8).reshape(bs * kv, -1)
    Ksc = kv_sc.view(torch.uint8).reshape(bs * kv, -1)
    fp4_per_tok = Kfp4.shape[1]; sc_per_tok = Ksc.shape[1]

    buf_key = (bs, kv)
    if buf_key not in _hip_cache:
        scores = torch.empty(bs, NQ, kv, dtype=torch.bfloat16, device="cuda")
        p_fp4 = torch.empty(bs, NQ, kv // 2, dtype=torch.uint8, device="cuda")
        p_sc  = torch.empty(bs, NQ, kv // 32, dtype=torch.uint8, device="cuda")
        out = torch.empty(bs, NQ, VD, dtype=torch.bfloat16, device="cuda")
        _hip_cache[buf_key] = (scores, p_fp4, p_sc, out)

    scores, p_fp4, p_sc, out = _hip_cache[buf_key]
    Kptr = Kfp4.data_ptr(); Ksptr = Ksc.data_ptr()

    _launch_qk(q_bf16.contiguous().data_ptr(), Kptr, Ksptr,
               scores.data_ptr(), NQ, kv, QKD, bs,
               NQ * QKD, kv * fp4_per_tok, kv * sc_per_tok, sc_per_tok, NQ * kv)
    _launch_sm(scores.data_ptr(), p_fp4.data_ptr(), p_sc.data_ptr(),
               SM_SCALE, bs, NQ, kv)
    _launch_pv(p_fp4.data_ptr(), p_sc.data_ptr(), Kptr, Ksptr, out.data_ptr(),
               NQ, VD, kv, bs,
               NQ * (kv // 2), NQ * (kv // 32),
               kv * fp4_per_tok, fp4_per_tok, kv * sc_per_tok, sc_per_tok, NQ * VD)
    return out

# =========================================================================
# FP8 aiter fallback with metadata caching (for kv > 2048)
# =========================================================================
import aiter
from aiter import dtypes as aiter_dtypes
from aiter.mla import mla_decode_fwd
from aiter import get_mla_metadata_info_v1, get_mla_metadata_v1
from reference import PAGE_SIZE, NUM_KV_SPLITS, SM_SCALE as REF_SM_SCALE

FP8_DTYPE = aiter_dtypes.fp8

def _fp8_quantize(tensor):
    flat = tensor.view(-1, tensor.shape[-1])
    fp8_out, scale = aiter.per_tensor_quant_hip(flat, quant_dtype=FP8_DTYPE)
    return fp8_out.view(tensor.shape), scale

_fp8_cache = {}

def _fp8_kernel(data):
    q, kv_data, qo_indptr, kv_indptr, config = data
    bs = config["batch_size"]; nq = config["num_heads"]; nkv = config["num_kv_heads"]
    dq = config["qk_head_dim"]; dv = config["v_head_dim"]
    q_seq_len = config["q_seq_len"]
    total_kv = int(kv_indptr[-1].item())

    q_fp8, q_scale = _fp8_quantize(q)
    kv_fp8, kv_scale = kv_data["fp8"]

    key = (bs, total_kv)
    if key not in _fp8_cache:
        kv_indices = torch.arange(total_kv, dtype=torch.int32, device="cuda")
        kv_last_page_len = (kv_indptr[1:] - kv_indptr[:-1]).to(torch.int32)
        info = get_mla_metadata_info_v1(bs, q_seq_len, nq, FP8_DTYPE, FP8_DTYPE,
            is_sparse=False, fast_mode=False, num_kv_splits=NUM_KV_SPLITS,
            intra_batch_mode=True)
        w = [torch.empty(s, dtype=t, device="cuda") for s, t in info]
        get_mla_metadata_v1(qo_indptr, kv_indptr, kv_last_page_len,
            nq // nkv, nkv, True, w[0], w[2], w[1], w[3], w[4], w[5],
            page_size=PAGE_SIZE, kv_granularity=max(PAGE_SIZE, 16),
            max_seqlen_qo=q_seq_len, uni_seqlen_qo=q_seq_len, fast_mode=False,
            max_split_per_batch=NUM_KV_SPLITS, intra_batch_mode=True,
            dtype_q=FP8_DTYPE, dtype_kv=FP8_DTYPE)
        meta = dict(work_meta_data=w[0], work_indptr=w[1], work_info_set=w[2],
                    reduce_indptr=w[3], reduce_final_map=w[4], reduce_partial_map=w[5])
        kv_4d = kv_fp8.view(kv_fp8.shape[0], PAGE_SIZE, nkv, kv_fp8.shape[-1])
        o = torch.empty((q.shape[0], nq, dv), dtype=torch.bfloat16, device="cuda")
        _fp8_cache[key] = dict(kv_indices=kv_indices, kv_last_page_len=kv_last_page_len,
                               meta=meta, kv_4d=kv_4d, o=o)

    c = _fp8_cache[key]
    mla_decode_fwd(
        q_fp8.view(-1, nq, dq), c["kv_4d"], c["o"],
        qo_indptr, kv_indptr, c["kv_indices"], c["kv_last_page_len"],
        q_seq_len, page_size=PAGE_SIZE, nhead_kv=nkv,
        sm_scale=REF_SM_SCALE, logit_cap=0.0,
        num_kv_splits=NUM_KV_SPLITS,
        q_scale=q_scale, kv_scale=kv_scale,
        intra_batch_mode=True, **c["meta"])
    return c["o"]

# =========================================================================
# Hybrid dispatcher
# =========================================================================
_KV_THRESHOLD = 2048

def custom_kernel(data: input_t) -> output_t:
    config = data[4]
    kv = config["kv_seq_len"]
    if kv <= _KV_THRESHOLD:
        return _hip_fp4_kernel(data)
    else:
        return _fp8_kernel(data)