#include "cache.h"
#include "ops.h"
#include "core/registration.h"

#include <torch/library.h>

// Note: overwrite the external definition for sharing same name between
// libraries use different ISAs.
#define TORCH_EXTENSION_NAME _C

#ifdef USE_ONEDNN
void release_dnnl_matmul_handler(int64_t handler);

int64_t create_onednn_scaled_mm_handler(const torch::Tensor& b,
                                        const torch::Tensor& b_scales,
                                        at::ScalarType output_type,
                                        bool dynamic_act_quant, bool use_azp,
                                        int64_t primitive_cache_size);

void onednn_scaled_mm(torch::Tensor& c, const torch::Tensor& a,
                      const torch::Tensor& a_scales,
                      const std::optional<torch::Tensor>& azp,
                      const std::optional<torch::Tensor>& azp_adj,
                      const std::optional<torch::Tensor>& bias,
                      const torch::Tensor& handler_tensor);

int64_t create_onednn_mm_handler(const torch::Tensor& b,
                                 int64_t primitive_cache_size);

void onednn_mm(torch::Tensor& c, const torch::Tensor& a,
               const std::optional<torch::Tensor>& bias,
               const torch::Tensor& handler_tensor);

bool is_onednn_acl_supported();

void static_scaled_int8_quant(torch::Tensor& out, const torch::Tensor& input,
                              const torch::Tensor& scale,
                              const std::optional<torch::Tensor>& azp);

void dynamic_scaled_int8_quant(torch::Tensor& out, const torch::Tensor& input,
                               torch::Tensor& scale,
                               const std::optional<torch::Tensor>& azp);
#endif // USE_ONEDNN

void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
                        torch::Tensor& kv_cache, double scale,
                        torch::Tensor& block_tables, torch::Tensor& seq_lens);

int64_t init_shm_manager(const std::string& name, const int64_t group_size,
                         const int64_t rank, const int64_t thread_num);

std::string join_shm_manager(int64_t handle, const std::string& name);

void shm_allreduce(int64_t handle, torch::Tensor& data);

void shm_gather(int64_t handle, torch::Tensor& data,
                const std::optional<std::vector<torch::Tensor>>& outputs,
                int64_t dst);

void shm_all_gather(int64_t handle, const torch::Tensor& data,
                    torch::Tensor& output);

void shm_send_tensor_list(int64_t handle,
                          const std::vector<torch::Tensor>& tensor_list,
                          int64_t dst);

std::vector<torch::Tensor> shm_recv_tensor_list(int64_t handle, int64_t src);

// SGL CPU kernels

at::Tensor weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2,
                                const std::optional<at::Tensor>& bias,
                                bool is_vnni);

at::Tensor convert_weight_packed(at::Tensor& weight);

at::Tensor fused_experts_cpu(
    at::Tensor& hidden_states, at::Tensor& w1, at::Tensor& w2,
    at::Tensor& topk_weights, at::Tensor& topk_ids, bool inplace,
    int64_t moe_comp_method, const std::optional<at::Tensor>& w1_scale,
    const std::optional<at::Tensor>& w2_scale,
    const std::optional<at::Tensor>& w1_zero,
    const std::optional<at::Tensor>& w2_zero,
    const std::optional<std::vector<int64_t>> block_size, bool is_vnni);

at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2,
                                     at::Tensor& scales2,
                                     const std::optional<at::Tensor>& bias,
                                     at::ScalarType out_dtype, bool is_vnni);

// Adapted from sglang: FP8 W8A16 kernel
at::Tensor fp8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
                             at::Tensor& scales2,
                             std::vector<int64_t> block_size,
                             const std::optional<at::Tensor>& bias,
                             at::ScalarType out_dtype, bool is_vnni);

// Adapted from sglang: INT4 W4A8 kernels
std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
    at::Tensor qweight,  // awq: (*, K, N / 8)  ||  gptq: (*, K / 8, N) , int32
    at::Tensor qzeros,   // awq: (*, K / group_size, N / 8) ||  gptq: (*, K /
                         // group_size, N / 8) , int32
    at::Tensor scales,   // awq: (*, K / group_size, N) ||  gptq: (*, K /
                         // group_size, N) , bfloat16
    int64_t quant_method_4bit);

at::Tensor int4_scaled_mm_cpu(at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros,
                              at::Tensor& w_scales,
                              std::optional<at::Tensor> bias);

void activation_lut_bf16(torch::Tensor& out, torch::Tensor& input,
                         const std::string& activation);

torch::Tensor get_scheduler_metadata(
    const int64_t num_req, const int64_t num_heads_q,
    const int64_t num_heads_kv, const int64_t head_dim,
    const torch::Tensor& seq_lens, at::ScalarType dtype,
    const torch::Tensor& query_start_loc, const bool casual,
    const int64_t window_size, const std::string& isa_hint,
    const bool enable_kv_split);

void cpu_attn_reshape_and_cache(const torch::Tensor& key,
                                const torch::Tensor& value,
                                torch::Tensor& key_cache,
                                torch::Tensor& value_cache,
                                const torch::Tensor& slot_mapping,
                                const std::string& isa, const double k_scale,
                                const double v_scale,
                                const std::string& kv_cache_dtype);

void cpu_attention_with_kv_cache(
    const torch::Tensor& query, const torch::Tensor& key_cache,
    const torch::Tensor& value_cache, torch::Tensor& output,
    const torch::Tensor& query_start_loc, const torch::Tensor& seq_lens,
    const double scale, const bool causal,
    const std::optional<torch::Tensor>& alibi_slopes,
    const int64_t sliding_window_left, const int64_t sliding_window_right,
    const torch::Tensor& block_table, const double softcap,
    const torch::Tensor& scheduler_metadata,
    const std::optional<torch::Tensor>& s_aux, const double k_scale,
    const double v_scale, const std::string& kv_cache_dtype);

// Note: just for avoiding importing errors
void placeholder_op() { TORCH_CHECK(false, "Unimplemented"); }

void cpu_gemm_wna16(const torch::Tensor& input, const torch::Tensor& q_weight,
                    torch::Tensor& output, const torch::Tensor& scales,
                    const std::optional<torch::Tensor>& zeros,
                    const std::optional<torch::Tensor>& g_idx,
                    const std::optional<torch::Tensor>& bias,
                    const int64_t pack_factor, const std::string& isa_hint);

void prepack_moe_weight(const torch::Tensor& weight,
                        torch::Tensor& packed_weight, const std::string& isa);

void cpu_fused_moe(torch::Tensor& output, const torch::Tensor& input,
                   const torch::Tensor& w13, const torch::Tensor& w2,
                   const std::optional<torch::Tensor>& w13_bias,
                   const std::optional<torch::Tensor>& w2_bias,
                   const torch::Tensor& topk_weights,
                   const torch::Tensor& topk_id, const bool skip_weighted,
                   const std::string& act, const std::string& isa);

void compute_slot_mapping_kernel_impl(const torch::Tensor query_start_loc,
                                      const torch::Tensor positions,
                                      const torch::Tensor block_table,
                                      torch::Tensor slot_mapping,
                                      const int64_t block_size);

void init_cpu_memory_env(std::vector<int64_t> node_ids);

namespace cpu_utils {
void eagle_prepare_inputs_padded_kernel_impl(
    const torch::Tensor& cu_num_draft_tokens,
    const torch::Tensor& valid_sampled_tokens_count,
    const torch::Tensor& query_start_loc_gpu,
    torch::Tensor& token_indices_to_sample,
    torch::Tensor& num_rejected_tokens_gpu, const int64_t num_reqs);
void eagle_prepare_next_token_padded_kernel_impl(
    const torch::Tensor& sampled_token_ids,
    const torch::Tensor& discard_request_mask,
    const torch::Tensor& backup_next_token_ids, torch::Tensor& next_token_ids,
    torch::Tensor& valid_sampled_tokens_count, const int64_t vocab_size,
    const int64_t num_sampled_tokens_per_req, const int64_t num_reqs);
void eagle_step_slot_mapping_metadata_kernel_impl(
    const torch::Tensor& positions, const torch::Tensor& block_table,
    torch::Tensor& seq_lens, torch::Tensor& out_clamped_positions,
    torch::Tensor& out_slot_mapping, const int64_t block_size,
    const int64_t max_model_len, const int64_t PAD_ID);
void copy_and_expand_eagle_inputs_kernel_impl(
    const torch::Tensor& target_token_ids,
    const torch::Tensor& target_positions, const torch::Tensor& next_token_ids,
    torch::Tensor& out_input_ids, torch::Tensor& out_positions,
    torch::Tensor& out_is_rejected_token_mask,
    torch::Tensor& out_is_masked_token_mask,
    torch::Tensor& out_new_token_indices,
    torch::Tensor& out_hidden_state_mapping,
    const torch::Tensor& query_start_loc, const torch::Tensor& query_end_loc,
    const int64_t padding_token_id, const int64_t parallel_drafting_token_id,
    const int64_t total_input_tokens,
    const int64_t num_padding_slots_per_request, const bool shift_input_ids);
void rejection_greedy_sample_kernel_impl(
    torch::Tensor& output_token_ids, const torch::Tensor& cu_num_draft_tokens,
    const torch::Tensor& draft_token_ids, const torch::Tensor& target_argmax,
    const torch::Tensor& bonus_token_ids,
    const std::optional<torch::Tensor>& is_greedy, const int64_t max_spec_len);
void rejection_random_sample_kernel_impl(
    torch::Tensor& output_token_ids, const torch::Tensor& cu_num_draft_tokens,
    const torch::Tensor& draft_token_ids,
    const std::optional<torch::Tensor>& draft_probs,
    const torch::Tensor& target_probs, const torch::Tensor& bonus_token_ids,
    const torch::Tensor& recovered_token_ids,
    const torch::Tensor& uniform_probs,
    const std::optional<torch::Tensor>& is_greedy, const int64_t max_spec_len,
    const int64_t vocab_size, const bool no_draft_probs);
void expand_kernel_impl(torch::Tensor& output, const torch::Tensor& input,
                        const torch::Tensor& cu_num_tokens,
                        const int64_t replace_from, const int64_t replace_to);
void sample_recovered_tokens_kernel_impl(
    torch::Tensor& output_token_ids, const torch::Tensor& cu_num_draft_tokens,
    const torch::Tensor& draft_token_ids,
    const std::optional<torch::Tensor>& draft_probs,
    const torch::Tensor& target_probs, const torch::Tensor& inv_q,
    const int64_t vocab_size, const bool no_draft_probs);
}  // namespace cpu_utils

TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
  // vLLM custom ops

  ops.def(
      "dynamic_4bit_int_moe("
      "Tensor x, Tensor topk_ids, Tensor topk_weights,"
      "Tensor w13_packed, Tensor w2_packed, int H, int I, int I2,"
      "int group_size, bool apply_router_weight_on_input, int activation_kind"
      ") -> Tensor");

  ops.impl("dynamic_4bit_int_moe", torch::kCPU, &dynamic_4bit_int_moe_cpu);

  // Activation ops

  // Activation function used in SwiGLU.
  ops.def("silu_and_mul(Tensor! out, Tensor input) -> ()");
  ops.impl("silu_and_mul", torch::kCPU, &silu_and_mul);

  // Activation function used in GeGLU with `none` approximation.
  ops.def("gelu_and_mul(Tensor! out, Tensor input) -> ()");
  ops.impl("gelu_and_mul", torch::kCPU, &gelu_and_mul);

  // Activation function used in GeGLU with `tanh` approximation.
  ops.def("gelu_tanh_and_mul(Tensor! out, Tensor input) -> ()");
  ops.impl("gelu_tanh_and_mul", torch::kCPU, &gelu_tanh_and_mul);

  // GELU implementation used in GPT-2.
  ops.def("gelu_new(Tensor! out, Tensor input) -> ()");
  ops.impl("gelu_new", torch::kCPU, &gelu_new);

  // Approximate GELU implementation.
  ops.def("gelu_fast(Tensor! out, Tensor input) -> ()");
  ops.impl("gelu_fast", torch::kCPU, &gelu_fast);

  // Quick GELU implementation.
  ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
  ops.impl("gelu_quick", torch::kCPU, &gelu_quick);

#if (defined(__aarch64__) && !defined(__APPLE__))

  ops.def(
      "activation_lut_bf16(Tensor! out, Tensor input, str activation)"
      " -> ()");
  ops.impl("activation_lut_bf16", torch::kCPU, &activation_lut_bf16);

#endif  // (defined(__aarch64__) && !defined(__APPLE__))

  // Layernorm
  // Apply Root Mean Square (RMS) Normalization to the input tensor.
  ops.def(
      "rms_norm(Tensor! out, Tensor input, Tensor weight, float epsilon) -> "
      "()");
  ops.impl("rms_norm", torch::kCPU, &rms_norm);

  // In-place fused Add and RMS Normalization.
  ops.def(
      "fused_add_rms_norm(Tensor! input, Tensor! residual, Tensor weight, "
      "float epsilon) -> ()");
  ops.impl("fused_add_rms_norm", torch::kCPU, &fused_add_rms_norm);

  // Rotary embedding
  // Apply GPT-NeoX or GPT-J style rotary embedding to query and key.
  ops.def(
      "rotary_embedding(Tensor positions, Tensor! query,"
      "                 Tensor!? key, int head_size,"
      "                 Tensor cos_sin_cache, bool is_neox, int "
      "rope_dim_offset=0, bool inverse=False) -> ()");
  ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);

  // Quantization (requires oneDNN)
#ifdef USE_ONEDNN
#if defined(__AVX512F__) || defined(__AVX2__) || \
    (defined(__aarch64__) && !defined(__APPLE__)) || defined(__powerpc64__)
  // Helper function to release oneDNN handlers
  ops.def("release_dnnl_matmul_handler(int handler) -> ()",
          &release_dnnl_matmul_handler);

  // Create oneDNN GEMM handler
  ops.def(
      "create_onednn_mm_handler(Tensor b, int "
      "primitive_cache_size) -> int",
      &create_onednn_mm_handler);

  // oneDNN GEMM
  ops.def(
      "onednn_mm(Tensor! c, Tensor a, Tensor? bias, "
      "Tensor handler_tensor) -> ()");
  ops.impl("onednn_mm", torch::kCPU, &onednn_mm);

  // Check if oneDNN was built with ACL backend
  ops.def("is_onednn_acl_supported() -> bool", &is_onednn_acl_supported);

  // Create oneDNN W8A8 handler
  ops.def(
      "create_onednn_scaled_mm_handler(Tensor b, Tensor b_scales, ScalarType "
      "output_type, bool dynamic_act_quant, bool use_azp, int "
      "primitive_cache_size) -> int",
      &create_onednn_scaled_mm_handler);

  // oneDNN scaled_mm for W8A8 with static per-tensor activation quantization
  ops.def(
      "onednn_scaled_mm(Tensor! c, Tensor a, Tensor a_scales, Tensor? azp, "
      "Tensor? azp_adj, Tensor? bias, Tensor handler_tensor) -> ()");
  ops.impl("onednn_scaled_mm", torch::kCPU, &onednn_scaled_mm);

  // Compute int8 quantized tensor for given scaling factor.
  ops.def(
      "static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale,"
      "Tensor? azp) -> ()");
  ops.impl("static_scaled_int8_quant", torch::kCPU, &static_scaled_int8_quant);

  // Compute int8 quantized tensor and scaling factor
  ops.def(
      "dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale, "
      "Tensor!? azp) -> ()");
  ops.impl("dynamic_scaled_int8_quant", torch::kCPU,
           &dynamic_scaled_int8_quant);
#endif
#endif // USE_ONEDNN

// SHM CCL
#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__))
  ops.def(
      "init_shm_manager(str name, int group_size, int rank, int thread_num) -> "
      "int",
      &init_shm_manager);
  ops.def("join_shm_manager(int handle, str name) -> str", &join_shm_manager);
  ops.def("shm_allreduce(int handle, Tensor! data) -> ()");
  ops.impl("shm_allreduce", torch::kCPU, &shm_allreduce);
  ops.def(
      "shm_gather(int handle, Tensor data, Tensor[](a!)? outputs, int dst) -> "
      "()");
  ops.impl("shm_gather", torch::kCPU, &shm_gather);
  ops.def(
      "shm_all_gather(int handle, Tensor data, Tensor! output) -> "
      "()");
  ops.impl("shm_all_gather", torch::kCPU, &shm_all_gather);
  ops.def(
      "shm_send_tensor_list(int handle, Tensor[](a) tensor_list, int dst) -> "
      "()");
  ops.impl("shm_send_tensor_list", torch::kCPU, &shm_send_tensor_list);
  ops.def("shm_recv_tensor_list(int handle, int src) -> Tensor[](a)",
          &shm_recv_tensor_list);
#endif  // #if defined(__AVX512F__) || defined(__aarch64__)

  // sgl-kernels
#if defined(__AVX512BF16__) && defined(__AVX512F__) && defined(__AVX512VNNI__)
  ops.def(
      "weight_packed_linear(Tensor(a0!) mat1, Tensor(a1!) mat2, Tensor(a2!)? "
      "bias, bool is_vnni) -> Tensor");
  ops.impl("weight_packed_linear", torch::kCPU, &weight_packed_linear);
  ops.def("convert_weight_packed(Tensor! weight) -> Tensor");
  ops.impl("convert_weight_packed", torch::kCPU, &convert_weight_packed);
  ops.def(
      "fused_experts_cpu(Tensor hidden_states, Tensor w1, Tensor w2, Tensor "
      "topk_weights, Tensor topk_ids, bool "
      "inplace, int moe_comp_method, Tensor? w1_scale, Tensor? w2_scale, "
      "Tensor? w1_zero, Tensor? w2_zero, int[]? block_size, bool is_vnni) -> "
      "Tensor");
  ops.impl("fused_experts_cpu", torch::kCPU, &fused_experts_cpu);
  ops.def(
      "int8_scaled_mm_with_quant(Tensor mat1, Tensor mat2, Tensor scales2, "
      "Tensor? bias, ScalarType out_dtype, bool is_vnni) -> Tensor");
  ops.impl("int8_scaled_mm_with_quant", torch::kCPU,
           &int8_scaled_mm_with_quant);

  // Adapted from sglang: INT4 W4A8 kernels
  ops.def(
      "convert_weight_packed_scale_zp(Tensor weight, Tensor qzeros, Tensor "
      "scales, int quant_method_4bit) -> (Tensor, "
      "Tensor, Tensor)");
  ops.impl("convert_weight_packed_scale_zp", torch::kCPU,
           &convert_weight_packed_scale_zp);

  ops.def(
      "int4_scaled_mm_cpu(Tensor(a0!) x, Tensor(a1!) w, Tensor(a2!) w_zeros, "
      "Tensor(a3!) w_scales, Tensor? bias) -> Tensor");
  ops.impl("int4_scaled_mm_cpu", torch::kCPU, &int4_scaled_mm_cpu);

  // Adapted from sglang: FP8 W8A16 kernel
  ops.def(
      "fp8_scaled_mm_cpu(Tensor(a0!) mat1, Tensor(a1!) mat2, Tensor(a2!) "
      "scales2, SymInt[] block_size, Tensor? bias, ScalarType out_dtype, "
      "bool is_vnni) -> Tensor");
  ops.impl("fp8_scaled_mm_cpu", torch::kCPU, &fp8_scaled_mm_cpu);
#endif

  // CPU attention kernels
  ops.def(
      "get_scheduler_metadata(int num_req, int num_heads_q, int num_heads_kv, "
      "int head_dim, Tensor seq_lens, ScalarType dtype, Tensor "
      "query_start_loc, bool casual, int window_size, str isa_hint, bool "
      "enable_kv_split) -> Tensor",
      &get_scheduler_metadata);
  ops.def(
      "cpu_attn_reshape_and_cache(Tensor key, Tensor value, Tensor(a2!) "
      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str isa, "
      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
      "()",
      &cpu_attn_reshape_and_cache);
  ops.def(
      "cpu_attention_with_kv_cache(Tensor query, Tensor key_cache, Tensor "
      "value_cache, Tensor(a3!) output, Tensor query_start_loc, Tensor "
      "seq_lens, float scale, bool causal, Tensor? alibi_slopes, SymInt "
      "sliding_window_left, SymInt sliding_window_right, Tensor block_table, "
      "float softcap, Tensor scheduler_metadata, Tensor? s_aux, "
      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
      "()",
      &cpu_attention_with_kv_cache);

  // placeholders
  ops.def("static_scaled_fp8_quant() -> ()", placeholder_op);
  ops.def("dynamic_scaled_fp8_quant() -> ()", placeholder_op);
  ops.def("dynamic_per_token_scaled_fp8_quant() -> ()", placeholder_op);

  // WNA16
#if defined(__AVX512F__)
  ops.def(
      "cpu_gemm_wna16(Tensor input, Tensor q_weight, Tensor(a2!) output, "
      "Tensor scales, Tensor? zeros, Tensor? g_idx, Tensor? bias, SymInt "
      "pack_factor, str isa_hint) -> ()");
  ops.impl("cpu_gemm_wna16", torch::kCPU, &cpu_gemm_wna16);
#endif

  // fused moe
#if defined(__AVX512F__)
  ops.def(
      "prepack_moe_weight(Tensor weight, Tensor(a1!) packed_weight, str isa) "
      "-> ()");
  ops.impl("prepack_moe_weight", torch::kCPU, &prepack_moe_weight);
  ops.def(
      "cpu_fused_moe(Tensor(a0!) output, Tensor input, Tensor w13, Tensor w2, "
      "Tensor? w13_bias, Tensor? w2_bias, Tensor topk_weights, Tensor topk_id, "
      "bool skip_weighted, "
      "str act, str isa) -> ()");
  ops.impl("cpu_fused_moe", torch::kCPU, &cpu_fused_moe);
#endif
  ops.def(
      "mla_decode_kvcache("
      "   Tensor! out, Tensor query, Tensor kv_cache,"
      "   float scale, Tensor block_tables, Tensor seq_lens) -> ()");
  ops.impl("mla_decode_kvcache", torch::kCPU, &mla_decode_kvcache);

  ops.def(
      "compute_slot_mapping_kernel_impl(Tensor query_start_loc, Tensor "
      "positions, Tensor block_table, Tensor(a3!) slot_mapping, SymInt "
      "block_size) -> ()",
      &compute_slot_mapping_kernel_impl);

  ops.def("init_cpu_memory_env(SymInt[] node_ids) -> ()", &init_cpu_memory_env);

  // Speculative decoding kernels
  ops.def(
      "eagle_prepare_inputs_padded_kernel_impl(Tensor cu_num_draft_tokens, "
      "Tensor valid_sampled_tokens_count, Tensor query_start_loc_gpu, "
      "Tensor(a3!) token_indices_to_sample, "
      "Tensor(a4!) num_rejected_tokens_gpu, "
      "SymInt num_reqs) -> ()",
      &cpu_utils::eagle_prepare_inputs_padded_kernel_impl);
  ops.def(
      "eagle_prepare_next_token_padded_kernel_impl("
      "Tensor sampled_token_ids, Tensor discard_request_mask, "
      "Tensor backup_next_token_ids, Tensor(a3!) next_token_ids, "
      "Tensor(a4!) valid_sampled_tokens_count, SymInt vocab_size, "
      "SymInt num_sampled_tokens_per_req, SymInt num_reqs) -> ()",
      &cpu_utils::eagle_prepare_next_token_padded_kernel_impl);
  ops.def(
      "eagle_step_slot_mapping_metadata_kernel_impl("
      "Tensor positions, Tensor block_table, Tensor(a2!) seq_lens, "
      "Tensor(a3!) out_clamped_positions, Tensor(a4!) out_slot_mapping, "
      "SymInt block_size, SymInt max_model_len, SymInt PAD_ID) -> ()",
      &cpu_utils::eagle_step_slot_mapping_metadata_kernel_impl);
  ops.def(
      "copy_and_expand_eagle_inputs_kernel_impl("
      "Tensor target_token_ids, Tensor target_positions, "
      "Tensor next_token_ids, Tensor(a3!) out_input_ids, "
      "Tensor(a4!) out_positions, "
      "Tensor(a5!) out_is_rejected_token_mask, "
      "Tensor(a6!) out_is_masked_token_mask, "
      "Tensor(a7!) out_new_token_indices, "
      "Tensor(a8!) out_hidden_state_mapping, "
      "Tensor query_start_loc, Tensor query_end_loc, "
      "SymInt padding_token_id, SymInt parallel_drafting_token_id, "
      "SymInt total_input_tokens, SymInt num_padding_slots_per_request, "
      "bool shift_input_ids) -> ()",
      &cpu_utils::copy_and_expand_eagle_inputs_kernel_impl);
  ops.def(
      "rejection_greedy_sample_kernel_impl("
      "Tensor(a0!) output_token_ids, Tensor cu_num_draft_tokens, "
      "Tensor draft_token_ids, Tensor target_argmax, "
      "Tensor bonus_token_ids, Tensor? is_greedy, "
      "SymInt max_spec_len) -> ()",
      &cpu_utils::rejection_greedy_sample_kernel_impl);
  ops.def(
      "rejection_random_sample_kernel_impl("
      "Tensor(a0!) output_token_ids, Tensor cu_num_draft_tokens, "
      "Tensor draft_token_ids, Tensor? draft_probs, "
      "Tensor target_probs, Tensor bonus_token_ids, "
      "Tensor recovered_token_ids, Tensor uniform_probs, "
      "Tensor? is_greedy, SymInt max_spec_len, SymInt vocab_size, "
      "bool no_draft_probs) -> ()",
      &cpu_utils::rejection_random_sample_kernel_impl);
  ops.def(
      "expand_kernel_impl(Tensor(a0!) output, Tensor input, "
      "Tensor cu_num_tokens, SymInt replace_from, "
      "SymInt replace_to) -> ()",
      &cpu_utils::expand_kernel_impl);
  ops.def(
      "sample_recovered_tokens_kernel_impl("
      "Tensor(a0!) output_token_ids, Tensor cu_num_draft_tokens, "
      "Tensor draft_token_ids, Tensor? draft_probs, "
      "Tensor target_probs, Tensor inv_q, SymInt vocab_size, "
      "bool no_draft_probs) -> ()",
      &cpu_utils::sample_recovered_tokens_kernel_impl);
}

REGISTER_EXTENSION(TORCH_EXTENSION_NAME)
