llama.cpp/src/llama-graph.h

#pragma once

#include <cstdint>

// note: do not add high-level objects here, such as llama_context, llama_kv_cache, etc.
//       not sure about llama_batch/llama_sbatch yet

struct ggml_cgraph;
struct ggml_context;
struct ggml_tensor;
struct ggml_backend_buffer;
struct llama_ubatch;

enum llama_graph_type {
    LLAMA_GRAPH_TYPE_DEFAULT,
    LLAMA_GRAPH_TYPE_ENCODER,
    LLAMA_GRAPH_TYPE_DECODER,
};

struct llama_graph_result {
    // important graph nodes
    ggml_tensor * t_logits      = nullptr;
    ggml_tensor * t_embd        = nullptr;
    ggml_tensor * t_embd_pooled = nullptr;
};

// TODO: can become more granular in the future
class llama_graph_i {
public:
    llama_graph_i(llama_graph_type type);
    virtual ~llama_graph_i() = default;

    llama_graph_type get_type() const { return type; }

protected:
    llama_graph_type type;

public:
    // callback that allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
    virtual void build_cb(
             ggml_tensor * cur,
              const char * name,
      const llama_ubatch & ubatch,
                     int   il) = 0;

    // apply control vector for layer il
    virtual ggml_tensor * build_cvec(
            ggml_context * ctx0,
             ggml_tensor * cur,
                     int   il) = 0;

    // do mat_mul, while optionally apply lora
    virtual ggml_tensor * build_lora_mm(
            ggml_context * ctx0,
             ggml_tensor * w,
             ggml_tensor * cur) = 0;

    // do mat_mul_id, while optionally apply lora
    virtual ggml_tensor * build_lora_mm_id(
            ggml_context * ctx0,
             ggml_tensor * w,   // struct ggml_tensor * as
             ggml_tensor * cur, // struct ggml_tensor * b
             ggml_tensor * ids) = 0;

    virtual ggml_tensor * build_rope_factors(int il) = 0;

    // note: optionally set the backend to be the same as the bbuf's backend
    virtual ggml_tensor * build_rope_shift(
            ggml_context * ctx0,
             ggml_tensor * cur,
             ggml_tensor * shift,
             ggml_tensor * factors,
             ggml_backend_buffer * bbuf) = 0;

    // graph build API (context-specific)

    virtual ggml_tensor * build_inp_embd(
            ggml_context * ctx0,
             ggml_tensor * tok_embd,
      const llama_ubatch & ubatch) = 0;

    virtual ggml_tensor * build_inp_pos(
            ggml_context * ctx0,
                 int32_t   n_tokens) = 0;

    virtual ggml_tensor * build_inp_pos_bucket(
            ggml_context * ctx0,
                 int32_t   n_tokens) = 0;

    virtual ggml_tensor * build_inp_out_ids(
            ggml_context * ctx0) = 0;

    virtual ggml_tensor * build_inp_mean(
            ggml_context * ctx0,
                 int32_t   n_tokens) = 0;

    virtual ggml_tensor * build_inp_cls(
            ggml_context * ctx0,
                 int32_t   n_tokens) = 0;

    virtual void build_attn_inp(
            ggml_context * ctx0,
                 int32_t   n_tokens,
                    bool   causal,
                    bool   swa) = 0;

    virtual ggml_tensor * build_attn(
            ggml_context * ctx0,
             ggml_cgraph * gf,
             ggml_tensor * wo,
             ggml_tensor * wo_b,
             ggml_tensor * q_cur,
             ggml_tensor * k_cur,
             ggml_tensor * v_cur,
             ggml_tensor * kq_b,
                 int32_t   n_tokens,
                 float     kq_scale,
                 int       il);

    virtual ggml_tensor * build_inp_self_k_shift(
            ggml_context * ctx0);

    virtual ggml_tensor * build_inp_embd_enc(
            ggml_context * ctx0);

    virtual ggml_tensor * build_inp_kq_mask_cross(
            ggml_context * ctx0,
                 int32_t   n_tokens);

    virtual ggml_tensor * build_inp_s_copy(
            ggml_context * ctx0);

    virtual ggml_tensor * build_inp_s_mask(
            ggml_context * ctx0);

    virtual ggml_tensor * build_copy_mask_state(
            ggml_context * ctx0,
             ggml_cgraph * gf,
             ggml_tensor * s,
             ggml_tensor * state_copy,
             ggml_tensor * state_mask,
                 int32_t   n_state,
                 int32_t   n_seqs);

    virtual ggml_tensor * build_mamba_layer(
            ggml_context * ctx0,
             ggml_cgraph * gf,
             ggml_tensor * cur,
             ggml_tensor * state_copy,
             ggml_tensor * state_mask,
      const llama_ubatch & ubatch,
                     int   il);

    virtual ggml_tensor * build_rwkv_token_shift_load(
            ggml_context * ctx0,
             ggml_cgraph * gf,
             ggml_tensor * state_copy,
             ggml_tensor * state_mask,
      const llama_ubatch & ubatch,
                     int   il);

    virtual ggml_tensor * build_rwkv_token_shift_store(
            ggml_context * ctx0,
             ggml_tensor * token_shift,
      const llama_ubatch & ubatch,
                     int   il);

    virtual ggml_tensor * build_rwkv6_time_mix(
            ggml_context * ctx0,
             ggml_cgraph * gf,
             ggml_tensor * cur,
             ggml_tensor * x_prev,
             ggml_tensor * state_copy,
             ggml_tensor * state_mask,
      const llama_ubatch & ubatch,
                     int   il);
};