#pragma once
#include "llama-kv-cache.h"
#include <vector>
// llama_kv_cache_msa
// uses two instances of llama_kv_cache, one for K/V tensors, and one for the MSA indexer tensors
// both receive identical sequence operations and identical ubatches, so their cell layouts stay in synced.
// the context also exposes per-ubatch pos - cell translation maps populated from llama_kv_cells via
// llama_kv_cache::get_cells(), which the model graph uses to run MSA block selection in position space
class llama_kv_cache_msa : public llama_memory_i {
public:
llama_kv_cache_msa(
const llama_model & model,
ggml_type type_k,
ggml_type type_v,
bool v_trans,
bool offload,
bool unified,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type,
const layer_filter_cb & filter,
const layer_filter_cb & filter_idx,
const layer_reuse_cb & reuse);
~llama_kv_cache_msa() = 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;
std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() 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_kv_cache_msa specific API
llama_kv_cache * get_base() const;
llama_kv_cache * get_idx () const;
uint32_t get_n_pad() const { return n_pad; }
uint32_t get_n_seq_max() const { return n_seq_max; }
uint32_t get_n_swa() const { return n_swa; }
llama_swa_type get_swa_type() const { return swa_type; }
private:
// keep the indexer KV cache hparams instance here as llama_kv_cache stores only a reference
llama_hparams hparams_idx;
const uint32_t n_stream = 1;
const uint32_t n_seq_max = 1;
const uint32_t n_pad = 1;
const uint32_t n_swa = 0;
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
std::unique_ptr<llama_kv_cache> kv_base;
std::unique_ptr<llama_kv_cache> kv_idx;
};
class llama_kv_cache_msa_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
// used for errors
llama_kv_cache_msa_context(llama_memory_status status);
// used to create a full-cache context
llama_kv_cache_msa_context(
llama_kv_cache_msa * kv);
// used to create an update context
llama_kv_cache_msa_context(
llama_kv_cache_msa * kv,
llama_context * lctx,
bool optimize);
// used to create a batch processing context from a batch
llama_kv_cache_msa_context(
llama_kv_cache_msa * kv,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_idx,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_msa_context();
// llama_memory_context_i
bool next() override;
bool apply() override;
llama_memory_status get_status() const override;
const llama_ubatch & get_ubatch() const override;
// llama_kv_cache_msa_context specific API
const llama_kv_cache_context * get_base() const;
const llama_kv_cache_context * get_idx () const;
// max position currently present in the cache plus one, padded MSA blocks are defined over token positions
// so the block-selection tensors are sized by this value rather than by the number of cells
uint32_t get_n_pos() const;
// position <-> cell translation maps, populated from the base cache cells
// the model graph relates cache contents to token positions only through these per ubatch inputs
// value for empty or other-sequence cells is 0 so consumers must mask them
void set_input_cell_pos(ggml_tensor * dst, const llama_ubatch * ubatch, int32_t div) const;
// positions without a cell map to cell 0, consumers must mask them assumes one sequence per stream
void set_input_pos_slot(ggml_tensor * dst, const llama_ubatch * ubatch) const;
void set_input_pos_mask(ggml_tensor * dst, const llama_ubatch * ubatch) const;
private:
llama_kv_cache_msa * kv;
// the index of the next ubatch to process
size_t i_next = 0;
std::vector<llama_ubatch> ubatches;
const llama_memory_context_ptr ctx_base;
const llama_memory_context_ptr ctx_idx;
const llama_memory_status status;
};