/* 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 <Python.h>
#include <torch/extension.h>

#include <vector>

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

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

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

#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);                                                                  \
  }

using fptr_t = int64_t;

/*
 * From csrc/activation
 */
void rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
void sgl_fused_add_rmsnorm(torch::Tensor input, torch::Tensor residual, torch::Tensor weight, double eps);
void gemma_rmsnorm(at::Tensor& output, at::Tensor& input, at::Tensor& weight, double eps, int64_t cuda_stream);
void gemma_fused_add_rmsnorm(at::Tensor& input, at::Tensor& residual, at::Tensor& weight, double eps,
                             int64_t cuda_stream);
void silu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
void gelu_tanh_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);
void gelu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream);

/*
 * From csrc/allreduce
 */
#ifdef USE_ROCM
// ROCM custom allreduce
fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data, const std::vector<std::string>& handles,
                      const std::vector<int64_t>& offsets, int64_t rank, bool full_nvlink);
void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out);
void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer, torch::Tensor& out);
void dispose(fptr_t _fa);
int64_t meta_size();
void register_buffer(fptr_t _fa, torch::Tensor& t, const std::vector<std::string>& handles,
                     const std::vector<int64_t>& offsets);
std::tuple<torch::Tensor, std::vector<int64_t>> get_graph_buffer_ipc_meta(fptr_t _fa);
void register_graph_buffers(fptr_t _fa, const std::vector<std::string>& handles,
                            const std::vector<std::vector<int64_t>>& offsets);
torch::Tensor allocate_meta_buffer(int64_t size);
torch::Tensor get_meta_buffer_ipc_handle(torch::Tensor& inp);
#else
// TRTLLM custom allreduce
fptr_t init_custom_ar(int64_t rank_id, int64_t world_size, torch::Tensor& rank_data, const std::vector<fptr_t>& buffers,
                      const std::vector<fptr_t>& tmp_result_buffers, const std::vector<fptr_t>& barrier_in,
                      const std::vector<fptr_t>& barrier_out);
void dispose(fptr_t _fa);
void all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out);
std::tuple<std::vector<int64_t>, std::vector<int64_t>> get_graph_buffer_ipc_meta(fptr_t _fa);
void register_graph_buffers(fptr_t _fa, const std::vector<std::vector<int64_t>>& handles,
                            const std::vector<std::vector<int64_t>>& offsets);
#endif

/*
 * From csrc/gemm
 */
torch::Tensor int8_scaled_mm(const torch::Tensor& mat_a, const torch::Tensor& mat_b, const torch::Tensor& scales_a,
                             const torch::Tensor& scales_b, const torch::Dtype& out_dtype,
                             const c10::optional<torch::Tensor>& bias);
torch::Tensor fp8_scaled_mm(const torch::Tensor& mat_a, const torch::Tensor& mat_b, const torch::Tensor& scales_a,
                            const torch::Tensor& scales_b, const torch::Dtype& out_dtype,
                            const c10::optional<torch::Tensor>& bias);
torch::Tensor fp8_blockwise_scaled_mm(const torch::Tensor& mat_a, const torch::Tensor& mat_b,
                                      const torch::Tensor& scales_a, const torch::Tensor& scales_b,
                                      const torch::Dtype& out_dtype);
void sgl_per_token_group_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s, int64_t group_size,
                                   double eps, double fp8_min, double fp8_max);
void sgl_per_tensor_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s, bool is_static);
void cublas_grouped_gemm(const std::vector<torch::Tensor>& inputs, const std::vector<torch::Tensor>& weights,
                         const std::vector<torch::Tensor>& outputs, const torch::Dtype& out_dtype,
                         int64_t cublas_handle, int64_t cuda_stream);

/*
 * From csrc/moe
 */
void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts, int64_t block_size,
                          torch::Tensor sorted_token_ids, torch::Tensor experts_ids, torch::Tensor num_tokens_post_pad,
                          torch::Tensor token_cnts_buffer, torch::Tensor cumsum_buffer);

/*
 * From csrc/speculative
 */
void tree_speculative_sampling_target_only(at::Tensor predicts, at::Tensor accept_index,
                                           at::Tensor accept_token_num,  // mutable
                                           at::Tensor candidates, at::Tensor retrive_index,
                                           at::Tensor retrive_next_token, at::Tensor retrive_next_sibling,
                                           at::Tensor uniform_samples, at::Tensor target_probs, at::Tensor draft_probs,
                                           bool deterministic = true, int64_t cuda_stream = 0);

void build_tree_kernel_efficient(at::Tensor parent_list, at::Tensor selected_index, at::Tensor verified_seq_len,
                                 at::Tensor tree_mask, at::Tensor positions, at::Tensor retrive_index,
                                 at::Tensor retrive_next_token, at::Tensor retrive_next_sibling, int64_t topk,
                                 int64_t depth, int64_t draft_token_num);

void build_tree_kernel(at::Tensor parent_list, at::Tensor selected_index, at::Tensor verified_seq_len,
                       at::Tensor tree_mask, at::Tensor positions, at::Tensor retrive_index, int64_t topk,
                       int64_t depth, int64_t draft_token_num);

/*
 * From FlashInfer
 */
void bmm_fp8(at::Tensor A, at::Tensor B, at::Tensor D, at::Tensor A_scale, at::Tensor B_scale,
             at::Tensor workspace_buffer, int64_t cublas_handle, int64_t cuda_stream);
void min_p_sampling_from_probs(at::Tensor probs, at::Tensor uniform_samples, at::Tensor samples,
                               std::optional<at::Tensor> maybe_min_p_arr, double min_p_val, bool deterministic,
                               int64_t cuda_stream);
// top k renorm probs
// patch here, cause flashinfer use unsigned int. but torch must use int64_t for extension.
void top_k_renorm_probs(at::Tensor probs, at::Tensor renorm_probs, std::optional<at::Tensor> maybe_top_k_arr,
                        unsigned int top_k_val, int64_t cuda_stream);
// patch here, cause flashinfer use unsigned int. but torch must use int64_t for extension.
inline void top_k_renorm_probs_wrapper(at::Tensor probs, at::Tensor renorm_probs,
                                       std::optional<at::Tensor> maybe_top_k_arr, int64_t top_k_val,
                                       int64_t cuda_stream) {
  top_k_renorm_probs(probs, renorm_probs, maybe_top_k_arr, static_cast<unsigned int>(top_k_val), cuda_stream);
}
void top_p_renorm_probs(at::Tensor probs, at::Tensor renorm_probs, std::optional<at::Tensor> maybe_top_p_arr,
                        double top_p_val, int64_t cuda_stream);
void top_k_top_p_sampling_from_probs(at::Tensor probs, at::Tensor uniform_samples, at::Tensor samples,
                                     at::Tensor success, std::optional<at::Tensor> maybe_top_k_arr, double top_k_val,
                                     std::optional<at::Tensor> maybe_top_p_arr, double top_p_val, bool deterministic,
                                     int64_t cuda_stream);
void top_p_sampling_from_probs(at::Tensor probs, at::Tensor uniform_samples, at::Tensor samples, at::Tensor success,
                               std::optional<at::Tensor> maybe_top_p_arr, double top_p_val, bool deterministic,
                               int64_t cuda_stream);
void apply_rope_pos_ids_cos_sin_cache(at::Tensor q, at::Tensor k, at::Tensor q_rope, at::Tensor k_rope,
                                      at::Tensor cos_sin_cache, at::Tensor pos_ids, bool interleave,
                                      int64_t cuda_stream);

/*
 * Other
 */
void lightning_attention_decode(const torch::Tensor& q, const torch::Tensor& k, const torch::Tensor& v,
                                const torch::Tensor& past_kv, const torch::Tensor& slope, torch::Tensor output,
                                torch::Tensor new_kv);

// sgl_per_token_quant_fp8
void sgl_per_token_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s);
