ik_llama.cpp/src/llama-context.h

#pragma once

#include "llama-impl.h"
#include "llama-cparams.h"
#include "llama-sampling.h"

struct llama_model;

#include <vector>
#include <map>
#include <set>
#include <memory>

struct llama_kv_cell {
    llama_pos pos   = -1;
    llama_pos delta = 0;
    int32_t   src   = 0; // used by recurrent state models to copy states

    std::set<llama_seq_id> seq_id;

    bool has_seq_id(const llama_seq_id & id) const {
        return seq_id.find(id) != seq_id.end();
    }

    bool is_empty() const {
        return seq_id.empty();
    }

    bool is_same_seq(const llama_kv_cell & other) const {
        return seq_id == other.seq_id;
    }
};

// ring-buffer of cached KV data
struct llama_kv_cache {
    bool has_shift = false;
    bool do_defrag = false;
    bool do_copy   = false;
    bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
    bool v_trans   = true;  // the value tensor is transposed

    // Note: The value of head isn't only used to optimize searching
    // for a free KV slot. llama_decode_internal also uses it, so it
    // cannot be freely changed after a slot has been allocated.
    uint32_t head = 0;
    uint32_t size = 0;
    uint32_t used = 0; // used cells (i.e. at least one seq_id)

    // computed before each graph build
    uint32_t n = 0;

    ggml_type type_k = GGML_TYPE_F16;
    ggml_type type_v = GGML_TYPE_F16;

    std::vector<llama_kv_cell> cells;

    std::vector<struct ggml_tensor *> k_l; // per layer
    std::vector<struct ggml_tensor *> v_l;
    std::vector<struct ggml_tensor *> s_l; // per layer recurrent state storage (Qwen3Next)

    std::vector<llama_split_tensor> split_k_l;
    std::vector<llama_split_tensor> split_v_l;

    std::vector<struct ggml_context *> ctxs;
    std::vector<ggml_backend_buffer_t> bufs;

    size_t total_size() const {
        size_t size = 0;
        for (ggml_backend_buffer_t buf : bufs) {
            size += ggml_backend_buffer_get_size(buf);
        }
        return size;
    }

    ~llama_kv_cache() {
        for (struct ggml_context * ctx : ctxs) {
            ggml_free(ctx);
        }
        for (ggml_backend_buffer_t buf : bufs) {
            ggml_backend_buffer_free(buf);
        }
    }
};

struct llama_control_vector {
    std::vector<struct ggml_tensor *> tensors; // per layer
    std::vector<struct ggml_context *> ctxs;
    std::vector<ggml_backend_buffer_t> bufs;

    int32_t layer_start = -1;
    int32_t layer_end   = -1;

    struct ggml_tensor * tensor_for(int il) const {
        if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
            return nullptr;
        }
        return tensors[il];
    }

    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
        ggml_tensor * layer_dir = tensor_for(il);
        if (layer_dir != nullptr) {
            cur = ggml_add(ctx, cur, layer_dir);
        }
        return cur;
    }

    ~llama_control_vector() {
        for (struct ggml_context * ctx : ctxs) {
            ggml_free(ctx);
        }
        for (ggml_backend_buffer_t buf : bufs) {
            ggml_backend_buffer_free(buf);
        }
    }
};

struct llama_context {

    llama_context(const llama_model & model);

    ~llama_context();

    const struct llama_model & model;

    struct llama_cparams        cparams;
    struct llama_sampling       sampling;
    struct llama_kv_cache       kv_self;
    struct llama_control_vector cvec;

    std::vector<float> scale_data;

    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;

    std::vector<ggml_backend_t> backends;
#ifdef GGML_USE_METAL
    ggml_backend_t backend_metal = nullptr;
#endif
#ifdef GGML_USE_BLAS
    ggml_backend_t backend_blas = nullptr;
#endif
    ggml_backend_t backend_cpu = nullptr;

    bool has_evaluated_once = false;

    int64_t t_start_us;
    int64_t t_load_us;
    int64_t t_p_eval_us = 0;
    int64_t t_eval_us   = 0;

    int64_t t_compute_start_us = 0;
    int64_t n_queued_tokens = 0;

    int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
    int32_t n_eval   = 0; // number of eval calls

    // host buffer for the model output (logits and embeddings)
    ggml_backend_buffer_t buf_output = nullptr;

    // decode output (2-dimensional array: [n_outputs][n_vocab])
    size_t  logits_size = 0; // capacity (of floats) for logits
    float * logits      = nullptr;

    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
    size_t  output_size = 0; // capacity (of tokens positions) for the output buffers
    int32_t n_outputs   = 0; // number of actually-used outputs in the current ubatch or last logical batch

    bool logits_all = false;

    // embeddings output (2-dimensional array: [n_outputs][n_embd])
    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
    size_t  embd_size = 0; // capacity (of floats) for embeddings
    float * embd      = nullptr;

    // sequence embeddings output (map of [n_embd] vectors)
    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
    std::map<llama_seq_id, std::vector<float>> embd_seq;

    // whether we are computing encoder output or decoder output
    bool is_encoding = false;

    // output of the encoder part of the encoder-decoder models
    std::vector<float> embd_enc;
    std::vector<std::set<llama_seq_id>> seq_ids_enc;

    // memory buffers used to evaluate the model
    std::vector<uint8_t> buf_compute_meta;
    ggml_backend_sched_t sched = nullptr;

    ggml_abort_callback abort_callback      = nullptr;
    void *              abort_callback_data = nullptr;

    // input tensors
    struct ggml_tensor * inp_tokens;      // I32 [n_batch]
    struct ggml_tensor * inp_embd;        // F32 [n_embd, n_batch]
    struct ggml_tensor * inp_pos;         // I32 [n_batch]
    struct ggml_tensor * inp_out_ids;     // I32 [n_outputs]
    struct ggml_tensor * inp_KQ_mask;     // F32 [kv_size, n_batch]
    struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch]
    struct ggml_tensor * inp_K_shift;     // I32 [kv_size]
    struct ggml_tensor * inp_mean;        // F32 [n_batch, n_batch]
    struct ggml_tensor * inp_cls;         // I32 [n_batch]
    struct ggml_tensor * inp_s_copy;      // I32 [kv_size]
    struct ggml_tensor * inp_s_mask;      // F32 [1, n_kv]
    struct ggml_tensor * inp_s_seq;       // I32 [n_kv, n_batch]
    struct ggml_tensor * inp_s_seq_qnext; // I32 [1, n_batch]
    struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
    struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
    struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
    struct ggml_tensor * inp_scale = nullptr; // F32 [n_tokens]

    ggml_backend_t ggml_backend_by_name(const char * name);

    struct Prev;
    std::unique_ptr<Prev> prev;

    void reset_scheduler();
    bool can_reuse_graph(const llama_batch & u_batch);

    struct CacheCopy {
        ggml_tensor * cpy = nullptr;
        size_t        step = 0;
    };
    std::vector<CacheCopy> cache_copies;

    bool update_cache_copies();

};