# Adapted from https://github.com/fla-org/flash-linear-attention/blob/main/fla/ops/common/chunk_delta_h.py
# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang

from typing import Optional, Tuple

import torch
import triton
import triton.language as tl

from sglang.srt.layers.attention.fla.index import (
    prepare_chunk_indices,
    prepare_chunk_offsets,
)
from sglang.srt.layers.attention.fla.op import exp, safe_exp
from sglang.srt.layers.attention.fla.utils import is_nvidia_hopper

NUM_WARPS = [2, 4] if is_nvidia_hopper else [2, 4, 8, 16]


@triton.heuristics(
    {
        "USE_G": lambda args: args["g"] is not None,
        "USE_GK": lambda args: args["gk"] is not None,
        "USE_INITIAL_STATE": lambda args: args["h0"] is not None,
        "STORE_FINAL_STATE": lambda args: args["ht"] is not None,
        "SAVE_NEW_VALUE": lambda args: args["v_new"] is not None,
        "IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
    }
)
# @triton.autotune(
#     configs=[
#         triton.Config({"BV": BV}, num_warps=num_warps, num_stages=num_stages)
#         for num_warps in [2, 4]
#         for num_stages in [2, 3, 4]
#         for BV in [32, 64]
#     ],
#     key=["H", "K", "V", "BT", "USE_G"],
#     use_cuda_graph=use_cuda_graph,
# )
@triton.jit(do_not_specialize=["T"])
def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
    k,
    v,
    w,
    v_new,
    g,
    gk,
    h,
    h0,
    ht,
    cu_seqlens,
    chunk_offsets,
    T,
    H: tl.constexpr,
    Hg: tl.constexpr,
    K: tl.constexpr,
    V: tl.constexpr,
    BT: tl.constexpr,
    BV: tl.constexpr,
    USE_G: tl.constexpr,
    USE_GK: tl.constexpr,
    USE_INITIAL_STATE: tl.constexpr,
    STORE_FINAL_STATE: tl.constexpr,
    SAVE_NEW_VALUE: tl.constexpr,
    IS_VARLEN: tl.constexpr,
):
    i_v, i_nh = tl.program_id(0), tl.program_id(1)
    i_n, i_h = i_nh // H, i_nh % H
    if IS_VARLEN:
        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(
            cu_seqlens + i_n + 1
        ).to(tl.int32)
        T = eos - bos
        NT = tl.cdiv(T, BT)
        boh = tl.load(chunk_offsets + i_n).to(tl.int32)
    else:
        bos, eos = i_n * T, i_n * T + T
        NT = tl.cdiv(T, BT)
        boh = i_n * NT

    # [BK, BV]
    b_h1 = tl.zeros([64, BV], dtype=tl.float32)
    if K > 64:
        b_h2 = tl.zeros([64, BV], dtype=tl.float32)
    if K > 128:
        b_h3 = tl.zeros([64, BV], dtype=tl.float32)
    if K > 192:
        b_h4 = tl.zeros([64, BV], dtype=tl.float32)

    # calculate offset
    h += ((boh * H + i_h) * K * V).to(tl.int64)
    v += ((bos * H + i_h) * V).to(tl.int64)
    k += ((bos * Hg + i_h // (H // Hg)) * K).to(tl.int64)
    w += ((bos * H + i_h) * K).to(tl.int64)
    if SAVE_NEW_VALUE:
        v_new += ((bos * H + i_h) * V).to(tl.int64)
    stride_v = H * V
    stride_h = H * K * V
    stride_k = Hg * K
    stride_w = H * K
    if USE_INITIAL_STATE:
        h0 = h0 + i_nh * K * V
    if STORE_FINAL_STATE:
        ht = ht + i_nh * K * V

    # load initial state
    if USE_INITIAL_STATE:
        p_h0_1 = tl.make_block_ptr(h0, (K, V), (V, 1), (0, i_v * BV), (64, BV), (1, 0))
        b_h1 += tl.load(p_h0_1, boundary_check=(0, 1)).to(tl.float32)
        if K > 64:
            p_h0_2 = tl.make_block_ptr(
                h0, (K, V), (V, 1), (64, i_v * BV), (64, BV), (1, 0)
            )
            b_h2 += tl.load(p_h0_2, boundary_check=(0, 1)).to(tl.float32)
        if K > 128:
            p_h0_3 = tl.make_block_ptr(
                h0, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0)
            )
            b_h3 += tl.load(p_h0_3, boundary_check=(0, 1)).to(tl.float32)
        if K > 192:
            p_h0_4 = tl.make_block_ptr(
                h0, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0)
            )
            b_h4 += tl.load(p_h0_4, boundary_check=(0, 1)).to(tl.float32)

    # main recurrence
    for i_t in range(NT):
        p_h1 = tl.make_block_ptr(
            h + i_t * stride_h, (K, V), (V, 1), (0, i_v * BV), (64, BV), (1, 0)
        )
        tl.store(p_h1, b_h1.to(p_h1.dtype.element_ty), boundary_check=(0, 1))
        if K > 64:
            p_h2 = tl.make_block_ptr(
                h + i_t * stride_h, (K, V), (V, 1), (64, i_v * BV), (64, BV), (1, 0)
            )
            tl.store(p_h2, b_h2.to(p_h2.dtype.element_ty), boundary_check=(0, 1))
        if K > 128:
            p_h3 = tl.make_block_ptr(
                h + i_t * stride_h, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0)
            )
            tl.store(p_h3, b_h3.to(p_h3.dtype.element_ty), boundary_check=(0, 1))
        if K > 192:
            p_h4 = tl.make_block_ptr(
                h + i_t * stride_h, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0)
            )
            tl.store(p_h4, b_h4.to(p_h4.dtype.element_ty), boundary_check=(0, 1))

        p_w = tl.make_block_ptr(
            w, (T, K), (stride_w, 1), (i_t * BT, 0), (BT, 64), (1, 0)
        )
        b_w = tl.load(p_w, boundary_check=(0, 1))
        b_v = tl.dot(b_w, b_h1.to(b_w.dtype))
        if K > 64:
            p_w = tl.make_block_ptr(
                w, (T, K), (stride_w, 1), (i_t * BT, 64), (BT, 64), (1, 0)
            )
            b_w = tl.load(p_w, boundary_check=(0, 1))
            b_v += tl.dot(b_w, b_h2.to(b_w.dtype))
        if K > 128:
            p_w = tl.make_block_ptr(
                w, (T, K), (stride_w, 1), (i_t * BT, 128), (BT, 64), (1, 0)
            )
            b_w = tl.load(p_w, boundary_check=(0, 1))
            b_v += tl.dot(b_w, b_h3.to(b_w.dtype))
        if K > 192:
            p_w = tl.make_block_ptr(
                w, (T, K), (stride_w, 1), (i_t * BT, 192), (BT, 64), (1, 0)
            )
            b_w = tl.load(p_w, boundary_check=(0, 1))
            b_v += tl.dot(b_w, b_h4.to(b_w.dtype))
        p_v = tl.make_block_ptr(
            v, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
        )
        b_v = tl.load(p_v, boundary_check=(0, 1)) - b_v

        if SAVE_NEW_VALUE:
            p_v = tl.make_block_ptr(
                v_new, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
            )
            tl.store(p_v, b_v.to(p_v.dtype.element_ty), boundary_check=(0, 1))

        last_idx = min((i_t + 1) * BT, T) - 1
        if USE_G:
            b_g_last = tl.load(g + bos * H + last_idx * H + i_h)
            p_g = tl.make_block_ptr(
                g + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)
            )
            b_g = tl.load(p_g, boundary_check=(0,))
            b_v = b_v * safe_exp(b_g_last - b_g)[:, None]
            b_g_last = exp(b_g_last)
            b_h1 = b_h1 * b_g_last
            if K > 64:
                b_h2 = b_h2 * b_g_last
            if K > 128:
                b_h3 = b_h3 * b_g_last
            if K > 192:
                b_h4 = b_h4 * b_g_last

        if USE_GK:
            o_k1 = tl.arange(0, 64)
            b_gk_last1 = tl.load(
                gk + (bos + last_idx) * H * K + i_h * K + o_k1,
                mask=(o_k1 < K),
                other=0.0,
            )
            b_h1 *= exp(b_gk_last1)[:, None]
            if K > 64:
                o_k2 = 64 + o_k1
                b_gk_last2 = tl.load(
                    gk + (bos + last_idx) * H * K + i_h * K + o_k2,
                    mask=(o_k2 < K),
                    other=0.0,
                )
                b_h2 *= exp(b_gk_last2)[:, None]
            if K > 128:
                o_k3 = 128 + o_k1
                b_gk_last3 = tl.load(
                    gk + (bos + last_idx) * H * K + i_h * K + o_k3,
                    mask=(o_k3 < K),
                    other=0.0,
                )
                b_h3 *= exp(b_gk_last3)[:, None]
            if K > 192:
                o_k4 = 192 + o_k1
                b_gk_last4 = tl.load(
                    gk + (bos + last_idx) * H * K + i_h * K + o_k4,
                    mask=(o_k4 < K),
                    other=0.0,
                )
                b_h4 *= exp(b_gk_last4)[:, None]
        b_v = b_v.to(k.dtype.element_ty)

        p_k = tl.make_block_ptr(
            k, (K, T), (1, stride_k), (0, i_t * BT), (64, BT), (0, 1)
        )
        b_k = tl.load(p_k, boundary_check=(0, 1))
        b_h1 += tl.dot(b_k, b_v)
        if K > 64:
            p_k = tl.make_block_ptr(
                k, (K, T), (1, stride_k), (64, i_t * BT), (64, BT), (0, 1)
            )
            b_k = tl.load(p_k, boundary_check=(0, 1))
            b_h2 += tl.dot(b_k, b_v)
        if K > 128:
            p_k = tl.make_block_ptr(
                k, (K, T), (1, stride_k), (128, i_t * BT), (64, BT), (0, 1)
            )
            b_k = tl.load(p_k, boundary_check=(0, 1))
            b_h3 += tl.dot(b_k, b_v)
        if K > 192:
            p_k = tl.make_block_ptr(
                k, (K, T), (1, stride_k), (192, i_t * BT), (64, BT), (0, 1)
            )
            b_k = tl.load(p_k, boundary_check=(0, 1))
            b_h4 += tl.dot(b_k, b_v)

    # epilogue
    if STORE_FINAL_STATE:
        p_ht = tl.make_block_ptr(ht, (K, V), (V, 1), (0, i_v * BV), (64, BV), (1, 0))
        tl.store(p_ht, b_h1.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
        if K > 64:
            p_ht = tl.make_block_ptr(
                ht, (K, V), (V, 1), (64, i_v * BV), (64, BV), (1, 0)
            )
            tl.store(p_ht, b_h2.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
        if K > 128:
            p_ht = tl.make_block_ptr(
                ht, (K, V), (V, 1), (128, i_v * BV), (64, BV), (1, 0)
            )
            tl.store(p_ht, b_h3.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
        if K > 192:
            p_ht = tl.make_block_ptr(
                ht, (K, V), (V, 1), (192, i_v * BV), (64, BV), (1, 0)
            )
            tl.store(p_ht, b_h4.to(p_ht.dtype.element_ty), boundary_check=(0, 1))


def chunk_gated_delta_rule_fwd_h(
    k: torch.Tensor,
    w: torch.Tensor,
    u: torch.Tensor,
    g: Optional[torch.Tensor] = None,
    gk: Optional[torch.Tensor] = None,
    initial_state: Optional[torch.Tensor] = None,
    output_final_state: bool = False,
    chunk_size: int = 64,  # SY: remove this argument and force chunk size 64?
    save_new_value: bool = True,
    cu_seqlens: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
    B, T, Hg, K, V = *k.shape, u.shape[-1]
    H = u.shape[-2]
    BT = chunk_size

    chunk_indices = (
        prepare_chunk_indices(cu_seqlens, chunk_size)
        if cu_seqlens is not None
        else None
    )
    # N: the actual number of sequences in the batch with either equal or variable lengths
    if cu_seqlens is None:
        N, NT, chunk_offsets = B, triton.cdiv(T, BT), None
    else:
        N, NT, chunk_offsets = (
            len(cu_seqlens) - 1,
            len(chunk_indices),
            prepare_chunk_offsets(cu_seqlens, BT),
        )
    assert K <= 256, "current kernel does not support head dimension larger than 256."

    h = k.new_empty(B, NT, H, K, V)
    final_state = (
        k.new_empty(N, H, K, V, dtype=torch.float32) if output_final_state else None
    )

    v_new = torch.empty_like(u) if save_new_value else None

    def grid(meta):
        return (triton.cdiv(V, meta["BV"]), N * H)

    chunk_gated_delta_rule_fwd_kernel_h_blockdim64[grid](
        k=k,
        v=u,
        w=w,
        v_new=v_new,
        g=g,
        gk=gk,
        h=h,
        h0=initial_state,
        ht=final_state,
        cu_seqlens=cu_seqlens,
        chunk_offsets=chunk_offsets,
        T=T,
        H=H,
        Hg=Hg,
        K=K,
        V=V,
        BT=BT,
        BV=32,
        num_warps=4,
        num_stages=2,
    )
    return h, v_new, final_state
