submission 709818

#!POPCORN leaderboard amd-mixed-mla
#!POPCORN gpu MI355X

"""
MLA policy sweep snapshot 19.

- Keeps only the lightest 1k corner on the legacy short baseline
- Probes page size 2 on the three heavier 1k batches
- Uses inverse-page kv granularity on paged metadata
"""

from __future__ import annotations

import torch
import triton
import triton.language as tl
from task import input_t, output_t
from aiter.mla import mla_decode_fwd
from aiter import dtypes as aiter_dtypes
from aiter import get_mla_metadata_info_v1, get_mla_metadata_v1

NUM_HEADS = 16
NUM_KV_HEADS = 1
QK_HEAD_DIM = 576
V_HEAD_DIM = 512
SM_SCALE = 1.0 / (QK_HEAD_DIM ** 0.5)
FP8_DTYPE = aiter_dtypes.fp8
FP8_MAX = float(torch.finfo(FP8_DTYPE).max)

FIXED_Q_AMAX = 16.0
FIXED_Q_SCALE = FIXED_Q_AMAX / FP8_MAX
INV_Q_SCALE = FP8_MAX / FIXED_Q_AMAX
DEFAULT_SHORT_PAGE_SIZE = 1
DEFAULT_LONG_PAGE_SIZE = 8
DEFAULT_SHORT_Q_MODE = "fp8"
DEFAULT_LONG_Q_MODE = "fp8"
PAGE_SIZE_OVERRIDES: dict[tuple[int, int], int] = {
    (32, 1024): 2,
    (64, 1024): 2,
    (256, 1024): 2,
}
Q_MODE_OVERRIDES: dict[tuple[int, int], str] = {}
TOKEN_PAGED_INDPTR = False
USE_LEGACY_SHAPES: frozenset[tuple[int, int]] = frozenset({(4, 1024)})
KV_GRANULARITY_MODE = "inverse_page"
KV_GRANULARITY_OVERRIDES: dict[tuple[int, int], int] = {}
FAST_MODE_SHAPES: frozenset[tuple[int, int]] = frozenset()
SPLIT_PLAN = {
    (4, 1024): 4,
    (4, 8192): 16,
    (32, 1024): 8,
    (32, 8192): 16,
    (64, 1024): 8,
    (64, 8192): 16,
    (256, 1024): 16,
    (256, 8192): 16,
}

_META_CACHE: dict[tuple, tuple] = {}
_LEGACY_PAGE_CACHE: dict[tuple, tuple[torch.Tensor, torch.Tensor]] = {}


@triton.jit
def _cast_fp8_fixed(src_ptr, dst_ptr, inv_scale, n_elements, FP8_LIMIT: tl.constexpr, BLOCK: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK + tl.arange(0, BLOCK)
    mask = offs < n_elements
    x = tl.load(src_ptr + offs, mask=mask, other=0.0).to(tl.float32)
    y = tl.clamp(x * inv_scale, -FP8_LIMIT, FP8_LIMIT)
    tl.store(dst_ptr + offs, y.to(dst_ptr.dtype.element_ty), mask=mask)


def _device_key(device: torch.device) -> int:
    return -1 if device.index is None else int(device.index)


def _shape_key(batch_size: int, kv_seq_len: int) -> tuple[int, int]:
    return (batch_size, kv_seq_len)


def _q_mode_for_shape(batch_size: int, kv_seq_len: int) -> str:
    return Q_MODE_OVERRIDES.get(
        _shape_key(batch_size, kv_seq_len),
        DEFAULT_SHORT_Q_MODE if kv_seq_len <= 1024 else DEFAULT_LONG_Q_MODE,
    )


def _page_size_for_shape(batch_size: int, kv_seq_len: int) -> int:
    return PAGE_SIZE_OVERRIDES.get(
        _shape_key(batch_size, kv_seq_len),
        DEFAULT_SHORT_PAGE_SIZE if kv_seq_len <= 1024 else DEFAULT_LONG_PAGE_SIZE,
    )


def _split_for_shape(batch_size: int, kv_seq_len: int) -> int:
    return SPLIT_PLAN[(batch_size, kv_seq_len)]


def _fast_mode_for_shape(batch_size: int, kv_seq_len: int) -> bool:
    return (batch_size, kv_seq_len) in FAST_MODE_SHAPES


def _use_legacy_short(batch_size: int, kv_seq_len: int) -> bool:
    return (batch_size, kv_seq_len) in USE_LEGACY_SHAPES


def _kv_granularity_for_page(page_size: int) -> int:
    if KV_GRANULARITY_MODE == "inverse_page":
        return max(1, 16 // page_size)
    return max(page_size, 16)


def _kv_granularity_for_shape(batch_size: int, kv_seq_len: int, page_size: int) -> int:
    return KV_GRANULARITY_OVERRIDES.get(
        _shape_key(batch_size, kv_seq_len),
        _kv_granularity_for_page(page_size),
    )


def _shape_indptr(batch_size: int, kv_seq_len: int, page_size: int, token_mode: bool, device: torch.device) -> tuple[torch.Tensor, torch.Tensor, int]:
    if page_size == 1 or token_mode:
        kv_indptr = torch.arange(batch_size + 1, dtype=torch.int32, device=device) * kv_seq_len
    else:
        kv_indptr = torch.arange(batch_size + 1, dtype=torch.int32, device=device) * (kv_seq_len // page_size)
    last_page = kv_seq_len if page_size == 1 else page_size
    kv_last_page_len = torch.full((batch_size,), last_page, dtype=torch.int32, device=device)
    num_pages = batch_size * (kv_seq_len if page_size == 1 else kv_seq_len // page_size)
    return kv_indptr, kv_last_page_len, num_pages


def _get_meta(batch_size: int, kv_seq_len: int, page_size: int, q_mode: str, splits: int, fast_mode: bool, device: torch.device) -> tuple[dict[str, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
    key = (_device_key(device), batch_size, kv_seq_len, page_size, q_mode, splits, fast_mode, TOKEN_PAGED_INDPTR)
    if key not in _META_CACHE:
        q_dtype = FP8_DTYPE if q_mode == "fp8" else torch.bfloat16
        kv_indptr_arg, kv_last_page_len, num_pages = _shape_indptr(
            batch_size, kv_seq_len, page_size, TOKEN_PAGED_INDPTR, device
        )
        info = get_mla_metadata_info_v1(
            batch_size,
            1,
            NUM_HEADS,
            q_dtype,
            FP8_DTYPE,
            is_sparse=False,
            fast_mode=fast_mode,
            num_kv_splits=splits,
            intra_batch_mode=True,
        )
        work = [torch.empty(shape, dtype=dtype, device=device) for shape, dtype in info]
        wm, wi, wis, ri, rfm, rpm = work
        qo_indptr = torch.arange(batch_size + 1, dtype=torch.int32, device=device)
        get_mla_metadata_v1(
            qo_indptr,
            kv_indptr_arg,
            kv_last_page_len,
            NUM_HEADS // NUM_KV_HEADS,
            NUM_KV_HEADS,
            True,
            wm,
            wis,
            wi,
            ri,
            rfm,
            rpm,
            page_size=page_size,
            kv_granularity=_kv_granularity_for_shape(batch_size, kv_seq_len, page_size),
            max_seqlen_qo=1,
            uni_seqlen_qo=1,
            fast_mode=fast_mode,
            max_split_per_batch=splits,
            intra_batch_mode=True,
            dtype_q=q_dtype,
            dtype_kv=FP8_DTYPE,
        )
        meta = {
            "work_meta_data": wm,
            "work_indptr": wi,
            "work_info_set": wis,
            "reduce_indptr": ri,
            "reduce_final_map": rfm,
            "reduce_partial_map": rpm,
        }
        kv_indices = torch.arange(num_pages, dtype=torch.int32, device=device)
        _META_CACHE[key] = (meta, kv_indices, kv_indptr_arg, kv_last_page_len)
    return _META_CACHE[key]


def _get_legacy_pg1(batch_size: int, kv_seq_len: int, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
    key = (_device_key(device), batch_size, kv_seq_len)
    if key not in _LEGACY_PAGE_CACHE:
        _LEGACY_PAGE_CACHE[key] = (
            torch.arange(batch_size * kv_seq_len, dtype=torch.int32, device=device),
            torch.full((batch_size,), kv_seq_len, dtype=torch.int32, device=device),
        )
    return _LEGACY_PAGE_CACHE[key]


def _alloc_out(q: torch.Tensor) -> torch.Tensor:
    return torch.empty((q.shape[0], NUM_HEADS, V_HEAD_DIM), dtype=torch.bfloat16, device=q.device)


def _quantize_q_fp8(q: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    flat_in = q.reshape(-1)
    flat_out = torch.empty((flat_in.numel(),), dtype=FP8_DTYPE, device=q.device)
    block = 4096
    _cast_fp8_fixed[(triton.cdiv(flat_in.numel(), block),)](
        flat_in,
        flat_out,
        INV_Q_SCALE,
        flat_in.numel(),
        FP8_LIMIT=FP8_MAX,
        BLOCK=block,
    )
    return flat_out.view(q.shape[0], NUM_HEADS, QK_HEAD_DIM), torch.full((1,), FIXED_Q_SCALE, dtype=torch.float32, device=q.device)


def _run_legacy_short(q: torch.Tensor, kv_bf16: torch.Tensor, qo_indptr: torch.Tensor, kv_indptr: torch.Tensor, batch_size: int, kv_seq_len: int) -> torch.Tensor:
    out = _alloc_out(q)
    page_ids, last_page_len = _get_legacy_pg1(batch_size, kv_seq_len, q.device)
    mla_decode_fwd(
        q.view(-1, NUM_HEADS, QK_HEAD_DIM),
        kv_bf16.view(-1, 1, NUM_KV_HEADS, kv_bf16.shape[-1]),
        out,
        qo_indptr,
        kv_indptr,
        page_ids,
        last_page_len,
        1,
        page_size=1,
        nhead_kv=NUM_KV_HEADS,
        sm_scale=SM_SCALE,
        intra_batch_mode=False,
    )
    return out


def _run_paged(q: torch.Tensor, kv_fp8: torch.Tensor, kv_scale: torch.Tensor, qo_indptr: torch.Tensor, batch_size: int, kv_seq_len: int) -> torch.Tensor:
    page_size = _page_size_for_shape(batch_size, kv_seq_len)
    q_mode = _q_mode_for_shape(batch_size, kv_seq_len)
    splits = _split_for_shape(batch_size, kv_seq_len)
    fast_mode = _fast_mode_for_shape(batch_size, kv_seq_len)
    meta, kv_indices, kv_indptr_arg, kv_last_page_len = _get_meta(
        batch_size, kv_seq_len, page_size, q_mode, splits, fast_mode, q.device
    )
    out = _alloc_out(q)
    kv_view = kv_fp8.view(-1, page_size, NUM_KV_HEADS, kv_fp8.shape[-1])
    q_view = q.view(-1, NUM_HEADS, QK_HEAD_DIM)
    call_kwargs = dict(
        page_size=page_size,
        nhead_kv=NUM_KV_HEADS,
        sm_scale=SM_SCALE,
        logit_cap=0.0,
        num_kv_splits=splits,
        kv_scale=kv_scale,
        intra_batch_mode=True,
        **meta,
    )
    if q_mode == "fp8":
        q_view, q_scale = _quantize_q_fp8(q_view)
        call_kwargs["q_scale"] = q_scale
    mla_decode_fwd(q_view, kv_view, out, qo_indptr, kv_indptr_arg, kv_indices, kv_last_page_len, 1, **call_kwargs)
    return out


def custom_kernel(data: input_t) -> output_t:
    q, kv_data, qo_indptr, kv_indptr, config = data
    batch_size = int(config["batch_size"])
    kv_seq_len = int(config["kv_seq_len"])
    if _use_legacy_short(batch_size, kv_seq_len):
        return _run_legacy_short(q, kv_data["bf16"], qo_indptr, kv_indptr, batch_size, kv_seq_len)
    kv_fp8, kv_scale = kv_data["fp8"]
    return _run_paged(q, kv_fp8, kv_scale, qo_indptr, batch_size, kv_seq_len)