/* Copyright 2025 SGLang Team. All Rights Reserved.

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.
==============================================================================*/

#pragma once

#include <ATen/ATen.h>
#include <ATen/Tensor.h>
#include <Python.h>
#include <torch/library.h>
#include <torch/torch.h>

#include <vector>

#include "sgl_kernel_torch_shim.h"

#define TORCH_LIBRARY_EXPAND(NAME, MODULE) TORCH_LIBRARY(NAME, MODULE)

#define _CONCAT(A, B) A##B
#define CONCAT(A, B) _CONCAT(A, B)

#define _STRINGIFY(A) #A
#define STRINGIFY(A) _STRINGIFY(A)

#define REGISTER_EXTENSION(NAME)                                                                      \
  PyMODINIT_FUNC CONCAT(PyInit_, NAME)() {                                                            \
    static struct PyModuleDef module = {PyModuleDef_HEAD_INIT, STRINGIFY(NAME), nullptr, 0, nullptr}; \
    return PyModule_Create(&module);                                                                  \
  }

/*
 * From flash-attention
 */
std::vector<at::Tensor> mha_fwd(
    at::Tensor& q,        // (b, s_q, h, d) or (total_q, h, d) if there is cu_seqlens_q
    const at::Tensor& k,  // (b_k, s_k, h_k, d) or (total_k, h_k, d) if there is cu_seqlens_k or (num_pages, page_size,
                          // h_k, d) if there is page_table.
    const at::Tensor& v,  // (b_k, s_k, h_k, dv) or (total_k, h_k, dv) if there is cu_seqlens_k or (num_pages,
                          // page_size, h_k, dv) if there is page_table.
    std::optional<const at::Tensor>&
        k_new_,  // (b, s_k_new, h_k, d) or (total_k_new, h_k, d) if there is cu_seqlens_k_new
    std::optional<const at::Tensor>&
        v_new_,  // (b, s_k_new, h_k, dv) or (total_k_new, h_k, dv) if there is cu_seqlens_k_new
    std::optional<const at::Tensor>& q_v_,           // (b, s_q, h, dv) or (total_q_new, h, dv) if there is cu_seqlens_q
    std::optional<at::Tensor>& out_,                 // (b, s_q, h, dv) or (total_q, h, dv) if there is cu_seqlens_q
    std::optional<const at::Tensor>& cu_seqlens_q_,  // b+1
    std::optional<const at::Tensor>& cu_seqlens_k_,  // b+1
    std::optional<const at::Tensor>& cu_seqlens_k_new_,  // b+1
    std::optional<const at::Tensor>&
        seqused_q_,  // b. If given, only this many elements of each batch element's queries and outputs are used.
    std::optional<const at::Tensor>&
        seqused_k_,  // b. If given, only this many elements of each batch element's keys are used.
    std::optional<int> max_seqlen_q_,
    // TODO: check if we need max_seqlen_k
    std::optional<int> max_seqlen_k_,
    std::optional<const at::Tensor>& page_table_,      // (b_k, max_num_pages_per_seq)
    std::optional<const at::Tensor>& kv_batch_idx_,    // b. indices to index into the KV cache
    std::optional<const at::Tensor>& leftpad_k_,       // b
    std::optional<const at::Tensor>& rotary_cos_,      // seqlen_ro x (rotary_dim / 2)
    std::optional<const at::Tensor>& rotary_sin_,      // seqlen_ro x (rotary_dim / 2)
    std::optional<const at::Tensor>& seqlens_rotary_,  // b
    std::optional<at::Tensor>& q_descale_,             // (b, h_k), not (b, h)
    std::optional<at::Tensor>& k_descale_,             // (b, h_k)
    std::optional<at::Tensor>& v_descale_,             // (b, h_k)
    float const softmax_scale,
    bool is_causal,
    int window_size_left,
    int window_size_right,
    float const softcap,
    bool const is_rotary_interleaved,  // if true, rotary combines indices 0 & 1, else indices 0 & rotary_dim / 2
    std::optional<at::Tensor>& scheduler_metadata_,  // (b + 1)
    int num_splits,
    std::optional<bool> pack_gqa_,
    int const sm_margin,
    std::optional<const at::Tensor>& sinks_);
