# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Memory-efficient attention for decoding.
It supports page size = 1.
"""

# Adapted from
# https://github.com/ModelTC/lightllm/blob/96353e868a840db4d103138caf15ed9dbea8c186/lightllm/models/deepseek2/triton_kernel/gqa_flash_decoding_stage1.py
# https://github.com/ModelTC/lightllm/blob/96353e868a840db4d103138caf15ed9dbea8c186/lightllm/models/deepseek2/triton_kernel/gqa_flash_decoding_stage2.py

import logging

import triton
import triton.language as tl

from sglang.srt.utils import is_hip

_is_hip = is_hip()

logger = logging.getLogger(__name__)


_MIN_BLOCK_KV = 32


@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,
    V_Buffer,
    sm_scale,
    kv_indptr,
    kv_indices,
    Att_Out,
    Att_Lse,
    num_kv_splits,
    stride_qbs,
    stride_qh,
    stride_buf_kbs,
    stride_buf_kh,
    stride_buf_vbs,
    stride_buf_vh,
    stride_mid_ob,
    stride_mid_oh,
    stride_mid_os,
    kv_group_num: tl.constexpr,
    BLOCK_DMODEL: tl.constexpr,
    BLOCK_DV: tl.constexpr,
    BLOCK_N: tl.constexpr,
    MIN_BLOCK_KV: tl.constexpr,
    logit_cap: tl.constexpr,
    Lk: tl.constexpr,
    Lv: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head = tl.program_id(1)
    split_kv_id = tl.program_id(2)

    cur_kv_head = cur_head // kv_group_num

    offs_d = tl.arange(0, BLOCK_DMODEL)
    offs_dv = tl.arange(0, BLOCK_DV)
    mask_d = offs_d < Lk
    mask_dv = offs_dv < Lv

    cur_batch_kv_start_idx = tl.load(kv_indptr + cur_batch)
    cur_batch_seq_len = tl.load(kv_indptr + cur_batch + 1) - cur_batch_kv_start_idx
    kv_splits = tl.load(num_kv_splits + cur_batch)

    off_q = cur_batch * stride_qbs + cur_head * stride_qh + offs_d

    kv_len_per_split = (
        tl.cdiv(tl.cdiv(cur_batch_seq_len, kv_splits), MIN_BLOCK_KV) * MIN_BLOCK_KV
    )
    split_kv_start = kv_len_per_split * split_kv_id
    split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)

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

    if split_kv_end > split_kv_start:
        q = tl.load(Q + off_q, mask=mask_d, other=0.0)
        for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
            offs_n = start_n + tl.arange(0, BLOCK_N)
            kv_loc = tl.load(
                kv_indices + cur_batch_kv_start_idx + offs_n,
                mask=offs_n < split_kv_end,
                other=0,
            )
            offs_buf_k = (
                kv_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[:, None] < split_kv_end) & (mask_d[None, :]),
                other=0.0,
            )
            qk = tl.sum(q[None, :] * k, 1)
            qk *= sm_scale

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

            qk = tl.where(offs_n < split_kv_end, qk, float("-inf"))

            offs_buf_v = (
                kv_loc[:, None] * stride_buf_vbs
                + cur_kv_head * stride_buf_vh
                + offs_dv[None, :]
            )
            v = tl.load(
                V_Buffer + offs_buf_v,
                mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
                other=0.0,
            )

            n_e_max = tl.maximum(tl.max(qk, 0), e_max)
            re_scale = tl.exp(e_max - n_e_max)
            p = tl.exp(qk - n_e_max)
            acc *= re_scale
            acc += tl.sum(p[:, None] * v, 0)

            e_sum = e_sum * re_scale + tl.sum(p, 0)
            e_max = n_e_max

        offs_mid_o = (
            cur_batch * stride_mid_ob
            + cur_head * stride_mid_oh
            + split_kv_id * stride_mid_os
            + offs_dv
        )

        tl.store(
            Att_Out + offs_mid_o,
            acc / e_sum,
            mask=(mask_dv),
        )

        offs_mid_o_1 = (
            cur_batch * stride_mid_ob
            + cur_head * stride_mid_oh
            + split_kv_id * stride_mid_os
        ) // Lv

        tl.store(
            Att_Lse + offs_mid_o_1,
            e_max + tl.log(e_sum),
        )


def _decode_att_m_fwd(
    q,
    k_buffer,
    v_buffer,
    att_out,
    att_lse,
    kv_indptr,
    kv_indices,
    num_kv_splits,
    max_kv_splits,
    sm_scale,
    logit_cap,
):
    BLOCK = 64
    # [TODO] work around SGPR limit on MI3xx
    if _is_hip:
        BLOCK = 8
    MAX_KV_SPLITS = max_kv_splits
    Lk = k_buffer.shape[-1]
    Lv = v_buffer.shape[-1]

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

    grid = (batch, head_num, MAX_KV_SPLITS)
    kv_group_num = q.shape[1] // k_buffer.shape[1]

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

    BLOCK_DMODEL = triton.next_power_of_2(Lk)
    BLOCK_DV = triton.next_power_of_2(Lv)

    _fwd_kernel_stage1[grid](
        q,
        k_buffer,
        v_buffer,
        sm_scale,
        kv_indptr,
        kv_indices,
        att_out,
        att_lse,
        num_kv_splits,
        q.stride(0),
        q.stride(1),
        k_buffer.stride(0),
        k_buffer.stride(1),
        v_buffer.stride(0),
        v_buffer.stride(1),
        att_out.stride(0),
        att_out.stride(1),
        att_out.stride(2),
        kv_group_num=kv_group_num,
        BLOCK_DMODEL=BLOCK_DMODEL,
        BLOCK_DV=BLOCK_DV,
        BLOCK_N=BLOCK,
        MIN_BLOCK_KV=_MIN_BLOCK_KV,
        logit_cap=logit_cap,
        num_warps=num_warps,
        num_stages=2,
        Lk=Lk,
        Lv=Lv,
    )


@triton.jit
def _fwd_grouped_kernel_stage1(
    Q,
    K_Buffer,
    V_Buffer,
    sm_scale,
    kv_indptr,
    kv_indices,
    Att_Out,
    Att_Lse,
    num_kv_splits,
    stride_qbs,
    stride_qh,
    stride_buf_kbs,
    stride_buf_kh,
    stride_buf_vbs,
    stride_buf_vh,
    stride_mid_ob,
    stride_mid_oh,
    stride_mid_os,
    kv_group_num: tl.constexpr,
    q_head_num: tl.constexpr,
    BLOCK_DMODEL: tl.constexpr,
    BLOCK_DPE: tl.constexpr,
    BLOCK_DV: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_H: tl.constexpr,
    MIN_BLOCK_KV: tl.constexpr,
    logit_cap: tl.constexpr,
    Lk: tl.constexpr,
    Lv: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head_id = tl.program_id(1)
    cur_kv_head = cur_head_id // tl.cdiv(kv_group_num, BLOCK_H)
    split_kv_id = tl.program_id(2)

    if BLOCK_H < kv_group_num:
        VALID_BLOCK_H: tl.constexpr = BLOCK_H
    else:
        VALID_BLOCK_H: tl.constexpr = kv_group_num
    cur_head = cur_head_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H)
    mask_h = cur_head < (cur_head_id + 1) * VALID_BLOCK_H
    mask_h = mask_h & (cur_head < q_head_num)

    offs_d = tl.arange(0, BLOCK_DMODEL)
    offs_dv = tl.arange(0, BLOCK_DV)
    mask_d = offs_d < Lk
    mask_dv = offs_dv < Lv

    cur_batch_kv_start_idx = tl.load(kv_indptr + cur_batch)
    cur_batch_seq_len = tl.load(kv_indptr + cur_batch + 1) - cur_batch_kv_start_idx
    kv_splits = tl.load(num_kv_splits + cur_batch)

    offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]

    if BLOCK_DPE > 0:
        offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
        mask_dpe = offs_dpe < Lk
        off_qpe = (
            cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_dpe[None, :]
        )

    kv_len_per_split = (
        tl.cdiv(tl.cdiv(cur_batch_seq_len, kv_splits), MIN_BLOCK_KV) * MIN_BLOCK_KV
    )
    split_kv_start = kv_len_per_split * split_kv_id
    split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)

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

    if split_kv_end > split_kv_start:
        q = tl.load(Q + offs_q, mask=(mask_h[:, None]) & (mask_d[None, :]), other=0.0)
        if BLOCK_DPE > 0:
            qpe = tl.load(
                Q + off_qpe, mask=(mask_h[:, None]) & (mask_dpe[None, :]), other=0.0
            )
        for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
            offs_n = start_n + tl.arange(0, BLOCK_N)
            kv_loc = tl.load(
                kv_indices + cur_batch_kv_start_idx + offs_n,
                mask=offs_n < split_kv_end,
                other=0,
            )
            offs_buf_k = (
                kv_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[None, :] < split_kv_end) & (mask_d[:, None]),
                other=0.0,
            )
            qk = tl.dot(q, k.to(q.dtype))
            if BLOCK_DPE > 0:
                offs_buf_kpe = (
                    kv_loc[None, :] * stride_buf_kbs
                    + cur_kv_head * stride_buf_kh
                    + offs_dpe[:, None]
                )
                kpe = tl.load(
                    K_Buffer + offs_buf_kpe,
                    mask=(offs_n[None, :] < split_kv_end) & (mask_dpe[:, None]),
                    other=0.0,
                )
                qk += tl.dot(qpe, kpe.to(qpe.dtype))
            qk *= sm_scale

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

            qk = tl.where(
                mask_h[:, None] & (offs_n[None, :] < split_kv_end), qk, float("-inf")
            )

            offs_buf_v = (
                kv_loc[:, None] * stride_buf_vbs
                + cur_kv_head * stride_buf_vh
                + offs_dv[None, :]
            )
            v = tl.load(
                V_Buffer + offs_buf_v,
                mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
                other=0.0,
            )

            n_e_max = tl.maximum(tl.max(qk, 1), e_max)
            re_scale = tl.exp(e_max - n_e_max)
            p = tl.exp(qk - n_e_max[:, None])
            acc *= re_scale[:, None]
            acc += tl.dot(p.to(v.dtype), v)

            e_sum = e_sum * re_scale + tl.sum(p, 1)
            e_max = n_e_max

        offs_mid_o = (
            cur_batch * stride_mid_ob
            + cur_head[:, None] * stride_mid_oh
            + split_kv_id * stride_mid_os
            + offs_dv[None, :]
        )

        tl.store(
            Att_Out + offs_mid_o,
            acc / e_sum[:, None],
            mask=(mask_h[:, None]) & (mask_dv[None, :]),
        )

        offs_mid_o_1 = (
            cur_batch * stride_mid_ob
            + cur_head * stride_mid_oh
            + split_kv_id * stride_mid_os
        ) // Lv

        tl.store(
            Att_Lse + offs_mid_o_1,
            e_max + tl.log(e_sum),
            mask=mask_h,
        )


def _decode_grouped_att_m_fwd(
    q,
    k_buffer,
    v_buffer,
    att_out,
    att_lse,
    kv_indptr,
    kv_indices,
    num_kv_splits,
    max_kv_splits,
    sm_scale,
    logit_cap,
):
    BLOCK = 32
    Lk = k_buffer.shape[-1]
    Lv = v_buffer.shape[-1]

    # [TODO] work around shmem limit on MI3xx
    if _is_hip and Lk >= 576:
        BLOCK = 16

    if Lk == 576:
        BLOCK_DMODEL = 512
        BLOCK_DPE = 64
    elif Lk == 288:
        BLOCK_DMODEL = 256
        BLOCK_DPE = 32
    else:
        BLOCK_DMODEL = triton.next_power_of_2(Lk)
        BLOCK_DPE = 0
    BLOCK_DV = triton.next_power_of_2(Lv)

    batch, head_num = q.shape[0], q.shape[1]
    kv_group_num = q.shape[1] // k_buffer.shape[1]

    BLOCK_H = 16
    MAX_KV_SPLITS = max_kv_splits
    grid = (
        batch,
        triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
        MAX_KV_SPLITS,
    )

    extra_kargs = {}
    num_stages = 2
    if _is_hip:
        # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
        # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
        extra_kargs = {"waves_per_eu": 1, "matrix_instr_nonkdim": 16, "kpack": 2}
        num_stages = 1

    _fwd_grouped_kernel_stage1[grid](
        q,
        k_buffer,
        v_buffer,
        sm_scale,
        kv_indptr,
        kv_indices,
        att_out,
        att_lse,
        num_kv_splits,
        q.stride(0),
        q.stride(1),
        k_buffer.stride(0),
        k_buffer.stride(1),
        v_buffer.stride(0),
        v_buffer.stride(1),
        att_out.stride(0),
        att_out.stride(1),
        att_out.stride(2),
        kv_group_num=kv_group_num,
        q_head_num=head_num,
        BLOCK_DMODEL=BLOCK_DMODEL,
        BLOCK_DPE=BLOCK_DPE,
        BLOCK_DV=BLOCK_DV,
        BLOCK_N=BLOCK,
        BLOCK_H=BLOCK_H,
        MIN_BLOCK_KV=_MIN_BLOCK_KV,
        logit_cap=logit_cap,
        num_warps=4,
        num_stages=num_stages,
        Lk=Lk,
        Lv=Lv,
        **extra_kargs,
    )


@triton.jit
def _fwd_kernel_stage2(
    Mid_O,
    Mid_O_1,
    O,
    kv_indptr,
    num_kv_splits,
    sink_ptr,
    stride_mid_ob,
    stride_mid_oh,
    stride_mid_os,
    stride_obs,
    stride_oh,
    MAX_KV_SPLITS: tl.constexpr,
    MIN_BLOCK_KV: tl.constexpr,
    BLOCK_DV: tl.constexpr,
    Lv: tl.constexpr,
    HAS_SINK: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head = tl.program_id(1)

    cur_batch_seq_len = tl.load(kv_indptr + cur_batch + 1) - tl.load(
        kv_indptr + cur_batch
    )
    kv_splits = tl.load(num_kv_splits + cur_batch)

    offs_d = tl.arange(0, BLOCK_DV)
    mask_d = offs_d < Lv

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

    offs_v = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + offs_d
    offs_logic = (cur_batch * stride_mid_ob + cur_head * stride_mid_oh) // Lv
    kv_len_per_split = (
        tl.cdiv(tl.cdiv(cur_batch_seq_len, kv_splits), MIN_BLOCK_KV) * MIN_BLOCK_KV
    )

    for split_kv_id in range(0, MAX_KV_SPLITS):
        split_kv_start = kv_len_per_split * split_kv_id
        split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)

        if split_kv_end > split_kv_start:
            tv = tl.load(
                Mid_O + offs_v + split_kv_id * stride_mid_os, mask=mask_d, other=0.0
            )
            tlogic = tl.load(Mid_O_1 + offs_logic + split_kv_id * stride_mid_os // Lv)
            n_e_max = tl.maximum(tlogic, e_max)

            old_scale = tl.exp(e_max - n_e_max)
            acc *= old_scale
            exp_logic = tl.exp(tlogic - n_e_max)
            acc += exp_logic * tv

            e_sum = e_sum * old_scale + exp_logic
            e_max = n_e_max

    if HAS_SINK:
        cur_sink = tl.load(sink_ptr + cur_head)
        e_sum += tl.exp(cur_sink - e_max)

    tl.store(
        O + cur_batch * stride_obs + cur_head * stride_oh + offs_d,
        acc / e_sum,
        mask=mask_d,
    )


def _decode_softmax_reducev_fwd(
    logits,
    lse,
    q,
    o,
    v_buffer,
    kv_indptr,
    num_kv_splits,
    max_kv_splits,
    sinks=None,
):
    batch, head_num = q.shape[0], q.shape[1]
    Lv = v_buffer.shape[-1]
    BLOCK_DV = triton.next_power_of_2(Lv)

    MAX_KV_SPLITS = max_kv_splits
    HAS_SINK = sinks is not None

    extra_kargs = {}
    if _is_hip:
        # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
        # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
        extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}

    grid = (batch, head_num)
    _fwd_kernel_stage2[grid](
        logits,
        lse,
        o,
        kv_indptr,
        num_kv_splits,
        sinks,
        logits.stride(0),
        logits.stride(1),
        logits.stride(2),
        o.stride(0),
        o.stride(1),
        MAX_KV_SPLITS=MAX_KV_SPLITS,
        MIN_BLOCK_KV=_MIN_BLOCK_KV,
        BLOCK_DV=BLOCK_DV,
        Lv=Lv,
        HAS_SINK=HAS_SINK,
        num_warps=4,
        num_stages=2,
        **extra_kargs,
    )


def decode_attention_fwd_normal(
    q,
    k_buffer,
    v_buffer,
    o,
    kv_indptr,
    kv_indices,
    attn_logits,
    attn_lse,
    num_kv_splits,
    max_kv_splits,
    sm_scale,
    logit_cap=0.0,
    sinks=None,
):
    _decode_att_m_fwd(
        q,
        k_buffer,
        v_buffer,
        attn_logits,
        attn_lse,
        kv_indptr,
        kv_indices,
        num_kv_splits,
        max_kv_splits,
        sm_scale,
        logit_cap,
    )
    _decode_softmax_reducev_fwd(
        attn_logits,
        attn_lse,
        q,
        o,
        v_buffer,
        kv_indptr,
        num_kv_splits,
        max_kv_splits,
        sinks,
    )


def decode_attention_fwd_grouped(
    q,
    k_buffer,
    v_buffer,
    o,
    kv_indptr,
    kv_indices,
    attn_logits,
    attn_lse,
    num_kv_splits,
    max_kv_splits,
    sm_scale,
    logit_cap=0.0,
    sinks=None,
):
    _decode_grouped_att_m_fwd(
        q,
        k_buffer,
        v_buffer,
        attn_logits,
        attn_lse,
        kv_indptr,
        kv_indices,
        num_kv_splits,
        max_kv_splits,
        sm_scale,
        logit_cap,
    )
    _decode_softmax_reducev_fwd(
        attn_logits,
        attn_lse,
        q,
        o,
        v_buffer,
        kv_indptr,
        num_kv_splits,
        max_kv_splits,
        sinks,
    )


def decode_attention_fwd(
    q,
    k_buffer,
    v_buffer,
    o,
    kv_indptr,
    kv_indices,
    attn_logits,
    attn_lse,
    num_kv_splits,
    max_kv_splits,
    sm_scale,
    logit_cap=0.0,
    sinks=None,
):
    assert max_kv_splits == attn_logits.shape[2]
    assert q.shape[0] <= kv_indptr.shape[0] - 1
    assert q.shape[0] <= attn_logits.shape[0]

    kv_group_num = q.shape[1] // v_buffer.shape[1]

    if kv_group_num == 1:
        # MHA
        decode_attention_fwd_normal(
            q,
            k_buffer,
            v_buffer,
            o,
            kv_indptr,
            kv_indices,
            attn_logits,
            attn_lse,
            num_kv_splits,
            max_kv_splits,
            sm_scale,
            logit_cap=logit_cap,
            sinks=sinks,
        )
    else:
        # GQA/MQA/MLA
        decode_attention_fwd_grouped(
            q,
            k_buffer,
            v_buffer,
            o,
            kv_indptr,
            kv_indices,
            attn_logits,
            attn_lse,
            num_kv_splits,
            max_kv_splits,
            sm_scale,
            logit_cap=logit_cap,
            sinks=sinks,
        )
