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

#include <ATen/core/dispatch/Dispatcher.h>
#include <torch/library.h>

#include "sgl_kernels_ops.h"

TORCH_LIBRARY_EXPAND(sgl_kernels, m) {
  // trt_reduce
  m.def(
      "init_custom_ar(int rank_id, int world_size, Tensor rank_data, int[] buffers, int[] tmp_result_buffers, int[] "
      "barrier_in, int[] barrier_out) -> int");
  m.impl("init_custom_ar", torch::kCUDA, &init_custom_ar);

  m.def("dispose", &dispose);

  m.def("all_reduce(int fa, Tensor inp, Tensor! out) -> ()");
  m.impl("all_reduce", torch::kCUDA, &all_reduce);

  m.def("get_graph_buffer_ipc_meta(int fa) -> (int[], int[])");
  m.impl("get_graph_buffer_ipc_meta", torch::kCUDA, &get_graph_buffer_ipc_meta);

  m.def("register_graph_buffers(int fa, int[][] handles, int[][] offsets) -> ()");
  m.impl("register_graph_buffers", torch::kCUDA, &register_graph_buffers);

  // moe_align_block_size
  m.def(
      "moe_align_block_size(Tensor topk_ids, int num_experts, int block_size, Tensor! sorted_token_ids, Tensor! "
      "experts_ids, Tensor! num_tokens_post_pad, Tensor! token_cnts_buffer, Tensor! cumsum_buffer) -> ()");
  m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);

  // int8_scaled_mm
  m.def(
      "int8_scaled_mm(Tensor mat_a, Tensor mat_b, Tensor scales_a, Tensor scales_b, ScalarType out_dtype, Tensor? "
      "bias) -> Tensor");
  m.impl("int8_scaled_mm", torch::kCUDA, &int8_scaled_mm);

  // fp8_scaled_mm
  m.def(
      "fp8_scaled_mm(Tensor mat_a, Tensor mat_b, Tensor scales_a, Tensor scales_b, ScalarType out_dtype, Tensor? "
      "bias) -> Tensor");
  m.impl("fp8_scaled_mm", torch::kCUDA, &fp8_scaled_mm);

  // lightning_attention_decode
  m.def(
      "lightning_attention_decode(Tensor q, Tensor k, Tensor v, Tensor past_kv, Tensor slope, Tensor! output, Tensor! "
      "new_kv) -> ()");
  m.impl("lightning_attention_decode", torch::kCUDA, &lightning_attention_decode);

  // rms norm
  m.def("rmsnorm(Tensor! output, Tensor input, Tensor weight, float eps, int cuda_stream) -> ()");
  m.impl("rmsnorm", torch::kCUDA, &rmsnorm);

  // fused rms norm
  m.def("fused_add_rmsnorm(Tensor! input, Tensor! residual, Tensor weight, float eps) -> ()");
  m.impl("fused_add_rmsnorm", torch::kCUDA, &sgl_fused_add_rmsnorm);

  // gemma rms norm
  m.def("gemma_rmsnorm(Tensor! output, Tensor input, Tensor weight, float eps, int cuda_stream) -> ()");
  m.impl("gemma_rmsnorm", torch::kCUDA, &gemma_rmsnorm);

  // fused gemma rms norm
  m.def("gemma_fused_add_rmsnorm(Tensor! input, Tensor! residual, Tensor weight, float eps, int cuda_stream) -> ()");
  m.impl("gemma_fused_add_rmsnorm", torch::kCUDA, &gemma_fused_add_rmsnorm);

  // silu and mul
  m.def("silu_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
  m.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);

  // gelu tanh and mul
  m.def("gelu_tanh_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
  m.impl("gelu_tanh_and_mul", torch::kCUDA, &gelu_tanh_and_mul);

  // gelu and mul
  m.def("gelu_and_mul(Tensor! out, Tensor input, int cuda_stream) -> ()");
  m.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);

  // bmm fp8
  m.def(
      "bmm_fp8(Tensor A, Tensor B, Tensor! D, Tensor A_scale, Tensor B_scale, Tensor workspace_buffer, int "
      "cublas_handle, int cuda_stream) -> ()");
  m.impl("bmm_fp8", torch::kCUDA, &bmm_fp8);

  // min p sampling from probs
  m.def(
      "min_p_sampling_from_probs(Tensor probs, Tensor uniform_samples, Tensor! samples, Tensor? maybe_min_p_arr, float "
      "min_p_val, bool deterministic, int cuda_stream) -> ()");
  m.impl("min_p_sampling_from_probs", torch::kCUDA, &min_p_sampling_from_probs);

  // top k renorm probs
  m.def(
      "top_k_renorm_probs_wrapper(Tensor probs, Tensor! renorm_probs, Tensor? maybe_top_k_arr, int top_k_val, int "
      "cuda_stream) -> ()");
  m.impl("top_k_renorm_probs_wrapper", torch::kCUDA, &top_k_renorm_probs_wrapper);

  // top p renorm probs
  m.def(
      "top_p_renorm_probs(Tensor probs, Tensor! renorm_probs, Tensor? maybe_top_p_arr, float top_p_val, int "
      "cuda_stream) -> ()");
  m.impl("top_p_renorm_probs", torch::kCUDA, &top_p_renorm_probs);

  // top k top p sampling from probs
  m.def(
      "top_k_top_p_sampling_from_probs(Tensor probs, Tensor uniform_samples, Tensor! samples, Tensor! success, Tensor? "
      "maybe_top_k_arr, float top_k_val, Tensor? maybe_top_p_arr, float top_p_val, bool deterministic, int "
      "cuda_stream) -> ()");
  m.impl("top_k_top_p_sampling_from_probs", torch::kCUDA, &top_k_top_p_sampling_from_probs);

  // top p sampling from probs
  m.def(
      "top_p_sampling_from_probs(Tensor probs, Tensor uniform_samples, Tensor! samples, Tensor! success, Tensor? "
      "maybe_top_p_arr, float top_p_val, bool deterministic, int cuda_stream) -> ()");
  m.impl("top_p_sampling_from_probs", torch::kCUDA, &top_p_sampling_from_probs);

  // apply rope with cos sin cache
  m.def(
      "apply_rope_pos_ids_cos_sin_cache(Tensor q, Tensor k, Tensor! q_rope, Tensor! k_rope, Tensor cos_sin_cache, "
      "Tensor pos_ids, bool interleave, int cuda_stream) -> ()");
  m.impl("apply_rope_pos_ids_cos_sin_cache", torch::kCUDA, &apply_rope_pos_ids_cos_sin_cache);
}

REGISTER_EXTENSION(_kernels)
