# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
#
# This file contains code copied from the flash-linear-attention project.
# The original source code was licensed under the MIT license and included
# the following copyright notice:
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# ruff: noqa: E501
from typing import Optional

import torch

from aphrodite.triton_utils import tl, triton

from .index import prepare_chunk_indices, prepare_chunk_offsets
from .op import exp, safe_exp
from .utils import is_nvidia_hopper, use_cuda_graph

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_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,
    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_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
    v += (bos * H + i_h) * V
    k += (bos * Hg + i_h // (H // Hg)) * K
    w += (bos * H + i_h) * K
    if SAVE_NEW_VALUE:
        v_new += (bos * H + i_h) * V
    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_v = tl.make_block_ptr(v, (T, V), (stride_v, 1), (i_t * BT, i_v * BV),
                                (BT, BV), (1, 0))
        p_v_new = tl.make_block_ptr(v_new, (T, V), (stride_v, 1),
                                    (i_t * BT, i_v * BV), (BT, BV),
                                    (1, 0)) if SAVE_NEW_VALUE else None
        b_v_new = tl.zeros([BT, BV], dtype=tl.float32)
        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_new += 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_new += 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_new += 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_new += tl.dot(b_w, b_h4.to(b_w.dtype))
        b_v_new = -b_v_new + tl.load(p_v, boundary_check=(0, 1))

        if SAVE_NEW_VALUE:
            p_v_new = 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_new,
                     b_v_new.to(p_v_new.dtype.element_ty),
                     boundary_check=(0, 1))

        if USE_G:
            last_idx = min((i_t + 1) * BT, T) - 1
            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_new = b_v_new * 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
        b_v_new = b_v_new.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_new)
        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_new)
        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_new)
        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_new)

    # 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,
    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,
        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)
    return h, v_new, final_state
