# Adapted from
# https://github.com/ModelTC/lightllm/blob/f2a54f0912293f683bf1d1695fd12c4098a5bf82/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py
# https://github.com/ModelTC/lightllm/blob/f2a54f0912293f683bf1d1695fd12c4098a5bf82/lightllm/models/llama/triton_kernel/token_attention_softmax_and_reducev.py
import torch
import triton
import triton.language as tl

from sglang.srt.managers.controller.infer_batch import global_server_args_dict

if global_server_args_dict.get("attention_reduce_in_fp32", False):
    REDUCE_TRITON_TYPE = tl.float32
    REDUCE_TORCH_TYPE = torch.float32
else:
    REDUCE_TRITON_TYPE = tl.float16
    REDUCE_TORCH_TYPE = torch.float16


@triton.jit
def tanh(x):
    # Tanh is just a scaled sigmoid
    return 2 * tl.sigmoid(2 * x) - 1


@triton.jit
def _fwd_kernel_stage1(
    Q,
    K_Buffer,
    sm_scale,
    Req_to_tokens,
    B_req_idx,
    B_Start_Loc,
    B_Seqlen,
    Att_Out,
    stride_req_to_tokens_b,
    stride_qbs,
    stride_qh,
    stride_buf_kbs,
    stride_buf_kh,
    att_stride_h,
    kv_group_num: tl.constexpr,
    BLOCK_DMODEL: tl.constexpr,
    BLOCK_N: tl.constexpr,
    logit_cap: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head = tl.program_id(1)
    start_n = tl.program_id(2)

    cur_kv_head = cur_head // kv_group_num

    offs_d = tl.arange(0, BLOCK_DMODEL)
    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
    cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
    cur_batch_req_idx = tl.load(B_req_idx + cur_batch)

    cur_batch_start_index = 0
    cur_batch_end_index = cur_batch_seq_len

    off_q = cur_batch * stride_qbs + cur_head * stride_qh + offs_d

    offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)

    block_stard_index = start_n * BLOCK_N
    block_mask = tl.where(block_stard_index < cur_batch_seq_len, 1, 0)

    for start_mark in range(0, block_mask, 1):
        q = tl.load(Q + off_q + start_mark).to(REDUCE_TRITON_TYPE)
        offs_n_new = cur_batch_start_index + offs_n
        k_loc = tl.load(
            Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n_new,
            mask=offs_n_new < cur_batch_end_index,
            other=0,
        )
        offs_buf_k = (
            k_loc[:, None] * stride_buf_kbs
            + cur_kv_head * stride_buf_kh
            + offs_d[None, :]
        )
        k = tl.load(
            K_Buffer + offs_buf_k,
            mask=offs_n_new[:, None] < cur_batch_end_index,
            other=0.0,
        ).to(REDUCE_TRITON_TYPE)
        att_value = tl.sum(q[None, :] * k, 1)
        att_value *= sm_scale

        if logit_cap > 0:
            att_value = logit_cap * tanh(att_value / logit_cap)

        off_o = cur_head * att_stride_h + (cur_batch_in_all_start_index + offs_n)
        tl.store(Att_Out + off_o, att_value, mask=offs_n_new < cur_batch_end_index)


@triton.jit
def _fwd_kernel_stage2(
    Logics,
    V_Buffer,
    Out,
    Req_to_tokens,
    B_req_idx,
    B_Start_Loc,
    B_Seqlen,
    stride_logic_h,
    stride_buf_vbs,
    stride_buf_vh,
    stride_obs,
    stride_oh,
    stride_req_to_token_b,
    kv_group_num: tl.constexpr,
    BLOCK_DMODEL: tl.constexpr,
    BLOCK_N: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head = tl.program_id(1)

    cur_kv_head = cur_head // kv_group_num

    cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
    cur_batch_start_loc = tl.load(B_Start_Loc + cur_batch)
    cur_batch_req_idx = tl.load(B_req_idx + cur_batch)

    offs_n = tl.arange(0, BLOCK_N)
    offs_d = tl.arange(0, BLOCK_DMODEL)

    offs_buf_v = cur_kv_head * stride_buf_vh + offs_d[None, :]
    v_ptrs = V_Buffer + offs_buf_v

    e_max = float("-inf")
    e_sum = 0.0
    acc = tl.zeros([BLOCK_DMODEL], dtype=tl.float32)

    for start_n in range(0, cur_batch_seq_len, BLOCK_N):
        start_n = tl.multiple_of(start_n, BLOCK_N)
        v_index = tl.load(
            Req_to_tokens
            + cur_batch_req_idx * stride_req_to_token_b
            + (start_n + offs_n),
            mask=(start_n + offs_n) < cur_batch_seq_len,
            other=0,
        )

        qk = tl.load(
            Logics
            + cur_head * stride_logic_h
            + (cur_batch_start_loc + start_n + offs_n),
            mask=start_n + offs_n < cur_batch_seq_len,
            other=float("-inf"),
        )

        n_e_max = tl.maximum(tl.max(qk, 0), e_max)
        old_scale = tl.exp(e_max - n_e_max)
        p = tl.exp(qk - n_e_max)
        e_sum = e_sum * old_scale + tl.sum(p, 0)
        v = tl.load(v_ptrs + v_index[:, None] * stride_buf_vbs)
        acc = acc * old_scale + tl.sum(p[:, None] * v, 0)
        e_max = n_e_max

    acc = acc / e_sum
    off_o = cur_batch * stride_obs + cur_head * stride_oh + offs_d
    out_ptrs = Out + off_o
    tl.store(out_ptrs, acc)


def _token_att_m_fwd(
    q,
    k_buffer,
    att_out,
    Req_to_tokens,
    B_req_idx,
    B_Start_Loc,
    B_Seqlen,
    max_len_in_batch,
    sm_scale,
    logit_cap,
):
    BLOCK = 32
    # shape constraints
    Lq, Lk = q.shape[-1], k_buffer.shape[-1]
    assert Lq == Lk
    assert Lk in {16, 32, 64, 128, 256}

    batch, head_num = B_req_idx.shape[0], q.shape[1]

    grid = (batch, head_num, triton.cdiv(max_len_in_batch, BLOCK))
    kv_group_num = q.shape[1] // k_buffer.shape[1]

    if kv_group_num == 1:
        num_warps = 4
    else:
        num_warps = 2

    _fwd_kernel_stage1[grid](
        q,
        k_buffer,
        sm_scale,
        Req_to_tokens,
        B_req_idx,
        B_Start_Loc,
        B_Seqlen,
        att_out,
        Req_to_tokens.stride(0),
        q.stride(0),
        q.stride(1),
        k_buffer.stride(0),
        k_buffer.stride(1),
        att_out.stride(0),
        kv_group_num=kv_group_num,
        BLOCK_DMODEL=Lk,
        BLOCK_N=BLOCK,
        logit_cap=logit_cap,
        num_warps=num_warps,
        num_stages=1,
    )


def _token_softmax_reducev_fwd(
    logics,
    v_buffer,
    o,
    req_to_tokens,
    b_req_idx,
    b_start_loc,
    b_seq_len,
):
    BLOCK = 64
    batch, head = b_seq_len.shape[0], logics.shape[0]
    grid = (batch, head, 1)
    kv_group_num = logics.shape[0] // v_buffer.shape[1]

    num_warps = 1

    _fwd_kernel_stage2[grid](
        logics,
        v_buffer,
        o,
        req_to_tokens,
        b_req_idx,
        b_start_loc,
        b_seq_len,
        logics.stride(0),
        v_buffer.stride(0),
        v_buffer.stride(1),
        o.stride(0),
        o.stride(1),
        req_to_tokens.stride(0),
        kv_group_num=kv_group_num,
        BLOCK_DMODEL=v_buffer.shape[-1],
        BLOCK_N=BLOCK,
        num_warps=num_warps,
        num_stages=3,
    )


def token_attention_fwd(
    q,
    k_buffer,
    v_buffer,
    o,
    req_to_token,
    b_req_idx,
    b_start_loc,
    b_seq_len,
    max_len_in_batch,
    total_num_tokens,
    sm_scale,
    logit_cap=-1,
    att_m=None,
):
    if att_m is None:
        att_m = torch.empty(
            (q.shape[-2], total_num_tokens), dtype=REDUCE_TORCH_TYPE, device="cuda"
        )

    _token_att_m_fwd(
        q,
        k_buffer,
        att_m,
        req_to_token,
        b_req_idx,
        b_start_loc,
        b_seq_len,
        max_len_in_batch,
        sm_scale,
        logit_cap,
    )
    _token_softmax_reducev_fwd(
        att_m,
        v_buffer,
        o,
        req_to_token,
        b_req_idx,
        b_start_loc,
        b_seq_len,
    )
