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#pragma once
#include "llama-batch.h"
#include "llama-graph.h"
#include "llama-kv-cache-unified.h"
#include "llama-memory.h"
#include "llama-memory-recurrent.h"
#include <memory>
#include <vector>
//
// llama_memory_hybrid
//
// utilizes instances of llama_memory_recurrent and llama_kv_cache_unified to
// support models where each layer may be either attention-based or recurrent
class llama_memory_hybrid : public llama_memory_i {
public:
// this callback is used to filter out layers that should not be included in the cache
using layer_filter_cb = std::function<bool(int32_t il)>;
llama_memory_hybrid(
const llama_model & model,
/* attn */
ggml_type type_k,
ggml_type type_v,
bool v_trans,
uint32_t kv_size,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
/* recurrent */
ggml_type type_r,
ggml_type type_s,
uint32_t rs_size,
/* common */
uint32_t n_seq_max,
bool offload,
bool unified,
/* layer filters */
layer_filter_cb && filter_attn = nullptr,
layer_filter_cb && filter_recr = nullptr);
~llama_memory_hybrid() = default;
//
// llama_memory_i
//
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_context_ptr init_full() override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
void clear(bool data) override;
bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
void seq_keep(llama_seq_id seq_id) override;
void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
llama_pos seq_pos_min(llama_seq_id seq_id) const override;
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
// state write/load
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
//
// llama_memory_hybrid specific API
//
llama_kv_cache_unified * get_mem_attn() const;
llama_memory_recurrent * get_mem_recr() const;
private:
const llama_hparams & hparams;
const std::unique_ptr<llama_kv_cache_unified> mem_attn;
const std::unique_ptr<llama_memory_recurrent> mem_recr;
};
class llama_memory_hybrid_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
// init failure
explicit llama_memory_hybrid_context(llama_memory_status status);
// init full
explicit llama_memory_hybrid_context(llama_memory_hybrid * mem);
// init update
explicit llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize);
// init success
llama_memory_hybrid_context(
llama_memory_hybrid * mem,
slot_info_vec_t sinfos_attn,
std::vector<llama_ubatch> ubatches);
~llama_memory_hybrid_context() = default;
bool next() override;
bool apply() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
//
// llama_memory_hybrid_context
//
const llama_kv_cache_unified_context * get_attn() const;
const llama_memory_recurrent_context * get_recr() const;
private:
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_context_ptr ctx_attn;
const llama_memory_context_ptr ctx_recr;
const llama_memory_status status;
};
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