# SPDX-License-Identifier: Apache-2.0

# Adapted from
# https://github.com/sgl-project/sglang/blob/9f635ea50de920aa507f486daafba26a5b837574/python/sglang/srt/layers/attention/triton_ops/decode_attention.py
# which was originally 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

# Changes:
# - Add support for page size >= 1.

# Copyright 2025 vLLM Team
# 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.
"""

import os
import logging
import torch
import triton
import triton.language as tl

from vllm.platforms import current_platform

is_hip_ = current_platform.is_rocm()
os.environ["TRITON_HIP_USE_NEW_STREAM_PIPELINE"] = f"0"

logger = logging.getLogger(__name__)

# TODO: Remove this when triton>=3.2.0. This issue will not affect performance
# and accuracy.
logger.warning(
    "The following error message 'operation scheduled before its operands' "
    "can be ignored.")


@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,
    Req_to_tokens,
    B_Seqlen,
    Att_Out,
    stride_req_to_tokens_b,
    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,
    NUM_KV_SPLITS: tl.constexpr,
    PAGE_SIZE: 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_seq_len = tl.load(B_Seqlen + cur_batch)
    cur_batch_req_idx = cur_batch

    off_q = cur_batch * stride_qbs + cur_head * stride_qh + offs_d
    q = tl.load(Q + off_q, mask=mask_d, other=0.0)

    kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_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)

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

    if split_kv_end > split_kv_start:
        for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
            offs_n = start_n + tl.arange(0, BLOCK_N)
            kv_page_number = tl.load(
                Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx +
                offs_n // PAGE_SIZE,
                mask=offs_n < split_kv_end,
                other=0,
            )
            kv_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
            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_Out + offs_mid_o_1,
            e_max + tl.log(e_sum),
        )


def _decode_att_m_fwd(
    q,
    k_buffer,
    v_buffer,
    att_out,
    Req_to_tokens,
    B_Seqlen,
    num_kv_splits,
    sm_scale,
    page_size,
    logit_cap,
):
    BLOCK = 64
    NUM_KV_SPLITS = num_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, NUM_KV_SPLITS)
    kv_group_num = q.shape[1] // k_buffer.shape[-2]

    num_warps = 4 if kv_group_num == 1 else 2

    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,
        Req_to_tokens,
        B_Seqlen,
        att_out,
        Req_to_tokens.stride(0),
        q.stride(0),
        q.stride(1),
        k_buffer.stride(-3),  # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
        k_buffer.stride(-2),  # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
        v_buffer.stride(-3),  # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
        v_buffer.stride(-2),  # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
        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,
        NUM_KV_SPLITS=NUM_KV_SPLITS,
        PAGE_SIZE=page_size,
        logit_cap=logit_cap,
        num_warps=num_warps,
        Lk=Lk,
        Lv=Lv,
    )



@triton.jit
def _fwd_kernel_stage2(
    Mid_O,
    o,
    B_Seqlen,
    stride_mid_ob,
    stride_mid_oh,
    stride_mid_os,
    stride_obs,
    stride_oh,
    NUM_KV_SPLITS: tl.constexpr,
    BLOCK_DV: tl.constexpr,
    Lv: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head = tl.program_id(1)

    cur_batch_seq_len = tl.load(B_Seqlen + 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

    for split_kv_id in range(0, NUM_KV_SPLITS):
        kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_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 + offs_logic + split_kv_id * stride_mid_os)
            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

    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,
    q,
    o,
    v_buffer,
    b_seq_len,
    num_kv_splits,
):
    batch, head_num = q.shape[0], q.shape[1]
    Lv = v_buffer.shape[-1]
    BLOCK_DV = triton.next_power_of_2(Lv)

    NUM_KV_SPLITS = num_kv_splits

    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": 0,
            "matrix_instr_nonkdim": 16,
            "kpack": 2
        }

    grid = (batch, head_num)
    _fwd_kernel_stage2[grid](
        logits,
        o,
        b_seq_len,
        logits.stride(0),
        logits.stride(1),
        logits.stride(2),
        o.stride(0),
        o.stride(1),
        NUM_KV_SPLITS=NUM_KV_SPLITS,
        BLOCK_DV=BLOCK_DV,
        Lv=Lv,
        num_warps=4,
        **extra_kargs,
    )


def decode_attention_fwd_normal(
    q,
    k_buffer,
    v_buffer,
    o,
    req_to_token,
    b_seq_len,
    attn_logits,
    num_kv_splits,
    sm_scale,
    page_size,
    logit_cap=0.0,
):
    _decode_att_m_fwd(
        q,
        k_buffer,
        v_buffer,
        attn_logits,
        req_to_token,
        b_seq_len,
        num_kv_splits,
        sm_scale,
        page_size,
        logit_cap,
    )
    _decode_softmax_reducev_fwd(attn_logits, q, o, v_buffer, b_seq_len,
                                num_kv_splits)




# opt
@triton.autotune(
    configs=[
        triton.Config({"BLOCK_N": 16}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 16}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 16}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=0, num_stages=1),
    ],
    key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh"]
)
@triton.jit
def _decode_v1_kernel_stage1_use_tc(
    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,
    q_head_num: tl.constexpr,
    BLOCK_DMODEL: tl.constexpr,
    BLOCK_DPE: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_H: tl.constexpr,
    SPLIT_K: tl.constexpr,
    PAGE_SIZE: tl.constexpr,
    logit_cap: tl.constexpr,
    Lk: 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_k_id = tl.program_id(2)

    #reduce_dtype = Att_Out.dtype.element_ty

    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)
    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_req_idx = cur_batch

    offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]
    q = tl.load(
        Q + offs_q, mask=(mask_h[:, None]) & (offs_d[None, :] < Lk), other=0.0
    )#.to(reduce_dtype)

    if BLOCK_DPE > 0:
        offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
        off_qpe = (
            cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_dpe[None, :]
        )
        qpe = tl.load(Q + off_qpe, mask=mask_h[:, None], other=0.0) #.to(reduce_dtype)

    kv_len_per_split = tl.cdiv(cur_batch_seq_len, SPLIT_K)
    split_k_start = kv_len_per_split * split_k_id
    split_k_end = tl.minimum(split_k_start + kv_len_per_split, cur_batch_seq_len)

    for start_n in range(split_k_start, split_k_end, BLOCK_N):
        offs_n = start_n + tl.arange(0, BLOCK_N)
        kv_page_number = tl.load(
                Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx +
                offs_n // PAGE_SIZE,
                mask=offs_n < split_k_end,
                other=0,
        )
        k_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
        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[None, :] < split_k_end) & (offs_d[:, None] < Lk),
            other=0.0,
        ) #.to(reduce_dtype)
        qk = tl.dot(q, k)
        if BLOCK_DPE > 0:
            offs_buf_kpe = (
                k_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_k_end,
                other=0.0,
            ) #.to(reduce_dtype)
            qk += tl.dot(qpe, kpe)
        qk *= sm_scale

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

        offs_o = cur_head[:, None] * att_stride_h + (
            cur_batch_in_all_start_index + offs_n[None, :]
        )

        tl.store(
            Att_Out + offs_o,
            qk,
            mask=mask_h[:, None] & (offs_n[None, :] < split_k_end),
        )

@triton.autotune(
    configs=[
        triton.Config({"BLOCK_N": 32}, num_warps=1, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=1, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 8}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 8}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 8}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 8}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 16}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 16}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 16}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 16}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=1, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=1, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=1, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=2, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=4, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=8, num_ldmatrixes=1, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=1, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=2, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=4, num_ldmatrixes=0, num_stages=1),
        triton.Config({"BLOCK_N": 512}, num_warps=8, num_ldmatrixes=0, num_stages=1),
    ],
    key=["B_Seqlen","stride_logic_h","stride_buf_vbs","stride_buf_vh"]
)
@triton.jit
def _decode_v1_kernel_stage2_use_tc(
    logits,
    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,
    q_head_num: tl.constexpr,
    BLOCK_DMODEL: tl.constexpr,
    BLOCK_H: tl.constexpr,
    PAGE_SIZE: tl.constexpr,
    Lv: tl.constexpr,
    BLOCK_N: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_kv_head = tl.program_id(1)
    cur_head = cur_kv_head * kv_group_num + tl.arange(0, BLOCK_H)
    mask_h = cur_head < (cur_kv_head + 1) * kv_group_num
    mask_h = mask_h & (cur_head < q_head_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 = cur_batch #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 = tl.zeros([BLOCK_H], dtype=tl.float32) - float("inf")
    e_sum = tl.zeros([BLOCK_H], dtype=tl.float32)
    acc = tl.zeros([BLOCK_H, 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_page_number = tl.load(
            Req_to_tokens
            + cur_batch_req_idx * stride_req_to_token_b
            + (start_n + offs_n) // PAGE_SIZE,
            mask=(start_n + offs_n) < cur_batch_seq_len,
            other=0,
        )
        v_loc = v_page_number * PAGE_SIZE + (start_n + offs_n) % PAGE_SIZE
        offs_qk = cur_head[:, None] * stride_logic_h + (
            cur_batch_start_loc + start_n + offs_n[None, :]
        )

        qk = tl.load(
            logits + offs_qk,
            mask=mask_h[:, None] & (start_n + offs_n[None, :] < cur_batch_seq_len),
            other=float("-inf"),
        ) #[head, block_n]

        n_e_max = tl.maximum(tl.max(qk, 1), e_max)
        old_scale = tl.exp(e_max - n_e_max)
        p = tl.exp(qk - n_e_max[:, None])
        e_sum = e_sum * old_scale + tl.sum(p, 1)
        v = tl.load(
            v_ptrs + v_loc[:, None] * stride_buf_vbs, mask=(offs_d[None, :] < Lv)
        ) #[block_n,head_dim]
        p = p.to(v.dtype)
        acc = acc * old_scale[:, None] + tl.dot(p, v)
        e_max = n_e_max

    acc = acc / e_sum[:, None]
    off_o = cur_batch * stride_obs + cur_head[:, None] * stride_oh + offs_d[None, :]
    out_ptrs = Out + off_o
    tl.store(out_ptrs, acc, mask=(mask_h[:, None]) & (offs_d[None, :] < Lv))

def _decode_v1_stage1_use_tc(
    q,
    k_buffer,
    att_out,
    Req_to_tokens,
    #B_req_idx,
    B_Start_Loc,
    B_Seqlen,
    sm_scale,
    page_size,
    num_kv_splits,
    logit_cap,
):
    Lk = k_buffer.shape[-1]

    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

    # batch, head_num = B_req_idx.shape[0], q.shape[1]
    batch, head_num = q.shape[0], q.shape[1]
    kv_group_num = q.shape[1] // k_buffer.shape[-2]
    SPLIT_K = num_kv_splits
    BLOCK_H = max(16, min(64, triton.next_power_of_2(kv_group_num)))
    grid = lambda META: (
        batch,
        triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
        SPLIT_K,
    )
    _decode_v1_kernel_stage1_use_tc[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(-3),
        k_buffer.stride(-2),
        att_out.stride(0),
        kv_group_num=kv_group_num,
        q_head_num=head_num,
        BLOCK_DMODEL=BLOCK_DMODEL,
        BLOCK_DPE=BLOCK_DPE,
        BLOCK_H=BLOCK_H,
        SPLIT_K=SPLIT_K,
        PAGE_SIZE=page_size,
        logit_cap=logit_cap,
        Lk=Lk,
        kpack=2,
    )
    return _decode_v1_kernel_stage1_use_tc.best_config


def _decode_v1_stage2_use_tc(
    logits,
    v_buffer,
    o,
    req_to_tokens,
    #b_req_idx,
    b_start_loc,
    b_seq_len,
    page_size,
):
    batch, head_num = b_seq_len.shape[0], logits.shape[0]
    kv_group_num = logits.shape[0] // v_buffer.shape[-2]
    BLOCK_H = max(16, triton.next_power_of_2(kv_group_num))
    grid = (batch, triton.cdiv(head_num, min(BLOCK_H, kv_group_num)), 1)

    Lv = v_buffer.shape[-1]
    BLOCK_DMODEL = triton.next_power_of_2(Lv)
    _decode_v1_kernel_stage2_use_tc[grid](
        logits,
        v_buffer,
        o,
        req_to_tokens,
        #b_req_idx,
        b_start_loc,
        b_seq_len,
        logits.stride(0),
        v_buffer.stride(-3),
        v_buffer.stride(-2),
        o.stride(0),
        o.stride(1),
        req_to_tokens.stride(0),
        kv_group_num=kv_group_num,
        q_head_num=head_num,
        BLOCK_DMODEL=BLOCK_DMODEL,
        BLOCK_H=BLOCK_H,
        PAGE_SIZE=page_size,
        Lv=Lv,
    )
    return _decode_v1_kernel_stage2_use_tc.best_config


def decode_attention_v1(
    q,
    k_buffer,
    v_buffer,
    o,
    req_to_token,
    #b_req_idx,
    b_start_loc,
    b_seq_len,
    attn_logits,
    num_kv_splits,
    sm_scale,
    page_size,
    logit_cap=0.0,
):
    # GQA/MQA/MLA
    _decode_v1_stage1_best_config = _decode_v1_stage1_use_tc(
                                        q,
                                        k_buffer,
                                        attn_logits,
                                        req_to_token,
                                        #b_req_idx,
                                        b_start_loc,
                                        b_seq_len,
                                        sm_scale,
                                        page_size,
                                        num_kv_splits,
                                        logit_cap,
                                    )
    _decode_v1_stage2_best_config = _decode_v1_stage2_use_tc(
                                        attn_logits,
                                        v_buffer,
                                        o,
                                        req_to_token,
                                        #b_req_idx,
                                        b_start_loc,
                                        b_seq_len,
                                        page_size,
                                    )
    return _decode_v1_stage1_best_config, _decode_v1_stage2_best_config



@triton.autotune(
    configs=[
        triton.Config({"BLOCK_N": 16, "BLOCK_DIM":64}, num_warps=2, num_stages=1),
        triton.Config({"BLOCK_N": 16, "BLOCK_DIM":64}, num_warps=4, num_stages=1),
        triton.Config({"BLOCK_N": 32, "BLOCK_DIM":64}, num_warps=2, num_stages=1),
        triton.Config({"BLOCK_N": 32, "BLOCK_DIM":64}, num_warps=4, num_stages=1),
        triton.Config({"BLOCK_N": 64, "BLOCK_DIM":32}, num_warps=2, num_stages=1),
        triton.Config({"BLOCK_N": 64, "BLOCK_DIM":32}, num_warps=4, num_stages=1),
        triton.Config({"BLOCK_N": 128, "BLOCK_DIM":32}, num_warps=2, num_stages=1),
        triton.Config({"BLOCK_N": 128, "BLOCK_DIM":32}, num_warps=4, num_stages=1),
        triton.Config({"BLOCK_N": 256, "BLOCK_DIM":32}, num_warps=2, num_stages=1),
        triton.Config({"BLOCK_N": 256, "BLOCK_DIM":32}, num_warps=4, num_stages=1),
    ],
    key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh", "stride_buf_vbs", "stride_buf_vh"]
)
@triton.jit
def _decode_v2_kernel_stage1_use_tc(
    Q,
    K_Buffer,
    V_Buffer,
    sm_scale,
    Req_to_tokens,
    # B_req_idx,
    B_Seqlen,
    Att_Out,
    stride_req_to_tokens_b,
    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_DIM: tl.constexpr,
    BLOCK_H: tl.constexpr,
    NUM_KV_SPLITS: tl.constexpr,
    PAGE_SIZE: 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_seq_len = tl.load(B_Seqlen + cur_batch)
    # cur_batch_req_idx = tl.load(B_req_idx + cur_batch)
    cur_batch_req_idx = cur_batch

    # offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]
    # q = tl.load(Q + offs_q, mask=(mask_h[:, None]) & (mask_d[None, :]), other=0.0)

    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, :]
        )
        qpe = tl.load(
            Q + off_qpe, mask=(mask_h[:, None]) & (mask_dpe[None, :]), other=0.0
        )

    kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_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)

    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)

    NUM_DIM_SPLIT = tl.cdiv(BLOCK_DMODEL, BLOCK_DIM)
    if split_kv_end > split_kv_start:
        for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
            offs_n = start_n + tl.arange(0, BLOCK_N)
            kv_page_number = tl.load(
                Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n // PAGE_SIZE,
                mask=offs_n < split_kv_end,
            )
            kv_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
            qk = tl.zeros([BLOCK_H, BLOCK_N], dtype=tl.float32)
            for i in range(0, NUM_DIM_SPLIT):
                offs_d = tl.arange(0, BLOCK_DIM) + i * BLOCK_DIM
                mask_d = offs_d < Lk
                offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None,:]
                q = tl.load(Q + offs_q, mask=(mask_h[:, None]) & (mask_d[None, :]), other=0.0)
                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_Out + offs_mid_o_1,
            e_max + tl.log(e_sum),
            mask=mask_h,
        )


def _decode_v2_stage1_use_tc(
    q,
    k_buffer,
    v_buffer,
    att_out,
    Req_to_tokens,
    # B_req_idx,
    B_Seqlen,
    num_kv_splits,
    sm_scale,
    page_size,
    logit_cap,
):

    Lk = k_buffer.shape[-1]
    Lv = v_buffer.shape[-1]

    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 = B_req_idx.shape[0], q.shape[1]
    batch, head_num = q.shape[0], q.shape[1]
    kv_group_num = q.shape[1] // k_buffer.shape[-2]
    BLOCK_H = 16
    NUM_KV_SPLITS = num_kv_splits
    grid = lambda META: (
        batch,
        triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
        NUM_KV_SPLITS,
    )

    _decode_v2_kernel_stage1_use_tc[grid](
        q,
        k_buffer,
        v_buffer,
        sm_scale,
        Req_to_tokens,
        # B_req_idx,
        B_Seqlen,
        att_out,
        Req_to_tokens.stride(0),
        q.stride(0),
        q.stride(1),
        k_buffer.stride(-3),
        k_buffer.stride(-2),
        v_buffer.stride(-3),
        v_buffer.stride(-2),
        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_H=BLOCK_H,
        NUM_KV_SPLITS=NUM_KV_SPLITS,
        PAGE_SIZE=page_size,
        logit_cap=logit_cap,
        Lk=Lk,
        Lv=Lv,
        kpack=2,
    )
    return _decode_v2_kernel_stage1_use_tc.best_config

@triton.autotune(
    configs=[
        triton.Config({}, num_warps=1, num_stages=1),
        triton.Config({}, num_warps=1, num_stages=1),
        triton.Config({}, num_warps=2, num_stages=1),
        triton.Config({}, num_warps=4, num_stages=1),
        triton.Config({}, num_warps=8, num_stages=1),
    ],
    key=["B_Seqlen", "stride_mid_ob", "stride_mid_oh", "stride_mid_os"]
)
@triton.jit
def _decode_v2_kernel_stage2(
    Mid_O,
    O,
    B_Seqlen,
    stride_mid_ob,
    stride_mid_oh,
    stride_mid_os,
    stride_obs,
    stride_oh,
    NUM_KV_SPLITS: tl.constexpr,
    BLOCK_DV: tl.constexpr,
    Lv: tl.constexpr,
):
    cur_batch = tl.program_id(0)
    cur_head = tl.program_id(1)

    cur_batch_seq_len = tl.load(B_Seqlen + 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

    for split_kv_id in range(0, NUM_KV_SPLITS):
        kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_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 + offs_logic + split_kv_id * stride_mid_os)
            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

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


def _decode_v2_stage2_use_tc(
    logits,
    q,
    o,
    v_buffer,
    b_seq_len,
    num_kv_splits,
):

    batch, head_num = q.shape[0], q.shape[1]
    Lv = v_buffer.shape[-1]
    BLOCK_DV = triton.next_power_of_2(Lv)

    NUM_KV_SPLITS = num_kv_splits

    grid = (batch, head_num)
    _decode_v2_kernel_stage2[grid](
        logits,
        o,
        b_seq_len,
        logits.stride(0),
        logits.stride(1),
        logits.stride(2),
        o.stride(0),
        o.stride(1),
        NUM_KV_SPLITS=NUM_KV_SPLITS,
        BLOCK_DV=BLOCK_DV,
        Lv=Lv,
    )

    return _decode_v2_kernel_stage2.best_config


def decode_attention_v2(
    q,
    k_buffer,
    v_buffer,
    o,
    req_to_token,
    # b_req_idx,
    b_seq_len,
    attn_logits,
    num_kv_splits,
    sm_scale,
    page_size,
    logit_cap=0.0,
):
    _decode_v2_stage1_best_config = _decode_v2_stage1_use_tc(
        q,
        k_buffer,
        v_buffer,
        attn_logits,
        req_to_token,
        # b_req_idx,
        b_seq_len,
        num_kv_splits,
        sm_scale,
        page_size,
        logit_cap,
    )
    _decode_v2_stage2_best_config = _decode_v2_stage2_use_tc(attn_logits, q, o, v_buffer, b_seq_len, num_kv_splits)
    return _decode_v2_stage1_best_config, _decode_v2_stage2_best_config


def decode_attentionv2_fwd(
    q,
    k_buffer,
    v_buffer,
    o,
    req_to_token,
    b_seq_len,
    attn_logits,
    num_kv_splits,
    sm_scale,
    page_size=1,
    logit_cap=0.0,
):
    assert num_kv_splits == attn_logits.shape[2]
    kv_group_num = q.shape[1] // v_buffer.shape[-2]
    b_start_loc = torch.arange(0, k_buffer.shape[0] * page_size, k_buffer.shape[0] * page_size // q.shape[0], device="cuda").to(torch.int32)
    if kv_group_num == 1:
        # MHA
        decode_attention_fwd_normal(
            q,
            k_buffer,
            v_buffer,
            o,
            req_to_token,
            b_seq_len,
            attn_logits,
            num_kv_splits,
            sm_scale,
            page_size,
            logit_cap,
        )
    else:
       #  GQA/MQA/MLA
        v2_tc_stage1_best_config, v2_tc_stage2_best_config = decode_attention_v2(
            q,
            k_buffer,
            v_buffer,
            o,
            req_to_token,
            b_seq_len,
            attn_logits,
            num_kv_splits,
            sm_scale,
            page_size,
            logit_cap,
        )
        return v2_tc_stage1_best_config, v2_tc_stage2_best_config

def decode_attentionv1_fwd(
    q,
    k_buffer,
    v_buffer,
    o,
    req_to_token,
    b_seq_len,
    attn_logits,
    num_kv_splits,
    sm_scale,
    page_size=1,
    logit_cap=0.0,
):
    assert num_kv_splits == attn_logits.shape[2]
    kv_group_num = q.shape[1] // v_buffer.shape[-2]
    b_start_loc = torch.arange(0, req_to_token.shape[0]*req_to_token.shape[1]*page_size, req_to_token.shape[0]*req_to_token.shape[1]* page_size // q.shape[0], device="cuda").to(torch.int32)
    if kv_group_num == 1:
        # MHA
        decode_attention_fwd_normal(
            q,
            k_buffer,
            v_buffer,
            o,
            req_to_token,
            b_seq_len,
            attn_logits,
            num_kv_splits,
            sm_scale,
            page_size,
            logit_cap,
        )
    else:
        attn_logits_v1 = torch.empty(
            (q.shape[1],req_to_token.shape[0]*req_to_token.shape[1]*page_size),
            dtype=torch.float32,
            device="cuda")
        v1_tc_stage1_best_config, v1_tc_stage2_best_config = decode_attention_v1(
            q,
            k_buffer,
            v_buffer,
            o,
            req_to_token,
            b_start_loc,
            b_seq_len,
            attn_logits_v1,
            num_kv_splits,
            sm_scale,
            page_size,
            logit_cap,
        )
        return v1_tc_stage1_best_config, v1_tc_stage2_best_config

