include llama.cpp since there were some edits
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#pragma once
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#include "llama-batch.h"
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#include "llama-graph.h"
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#include "llama-kv-cells.h"
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#include "llama-memory.h"
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#include <unordered_map>
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#include <vector>
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struct llama_cparams;
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struct llama_hparams;
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struct llama_model;
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struct llama_context;
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//
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// llama_kv_cache
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//
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class llama_kv_cache : public llama_memory_i {
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public:
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struct stream_copy_info {
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bool empty() const {
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assert(ssrc.size() == sdst.size());
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return ssrc.empty();
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}
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std::vector<uint32_t> ssrc;
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std::vector<uint32_t> sdst;
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};
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// for each ubatch, create a slot_info that contains information about where the ubatch should be inserted in the
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// KV cells. for example, cell indices for each token, such that: token[i] -> goes to cells[idxs[i]]
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struct slot_info {
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// data for ggml_set_rows
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using idx_vec_t = std::vector<uint32_t>;
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// number of streams: ns = s1 - s0 + 1
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uint32_t s0;
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uint32_t s1;
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std::vector<llama_seq_id> strm; // [ns]
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std::vector<idx_vec_t> idxs; // [ns]
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uint32_t head() const {
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GGML_ASSERT(idxs.size() == 1);
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GGML_ASSERT(!idxs[0].empty());
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return idxs[0][0];
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}
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void resize(size_t n) {
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strm.resize(n);
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idxs.resize(n);
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}
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size_t size() const {
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GGML_ASSERT(idxs.size() == strm.size());
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GGML_ASSERT(!idxs.empty());
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return idxs[0].size();
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}
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size_t n_stream() const {
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return strm.size();
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}
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bool empty() const {
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return idxs.empty();
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}
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void clear() {
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idxs.clear();
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}
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// check if indices are contiguous starting from head()
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bool is_contiguous() const {
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if (idxs.empty() || idxs[0].empty()) {
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return true;
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}
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if (idxs.size() > 1) {
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return false;
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}
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const uint32_t h = idxs[0][0];
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for (size_t i = 0; i < idxs[0].size(); ++i) {
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if (idxs[0][i] != h + i) {
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return false;
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}
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}
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return true;
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}
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};
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using slot_info_vec_t = std::vector<slot_info>;
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// TODO: refactor the memory instances to not depend on `llama_model`
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// instead pass all necessary info (e.g. hparams, dev layers, arch, etc.) directly
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// likely through `struct llama_memory_params`
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llama_kv_cache(
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const llama_model & model,
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bool no_alloc,
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const llama_hparams & hparams,
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ggml_type type_k,
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ggml_type type_v,
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bool v_trans,
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bool offload,
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bool unified,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_pad,
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uint32_t n_swa,
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llama_swa_type swa_type,
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llama_memory_t mem_other,
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const layer_filter_cb & filter,
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const layer_reuse_cb & reuse,
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const layer_share_cb & share,
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// a model can hold more than one cache, so the tensor names have to stay unique
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const char * name_tag = "");
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~llama_kv_cache() = default;
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//
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// llama_memory_i
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//
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llama_memory_context_ptr init_batch(
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llama_batch_allocr & balloc,
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uint32_t n_ubatch,
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bool embd_all) override;
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llama_memory_context_ptr init_full() override;
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llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
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bool get_can_shift() const override;
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void clear(bool data) override;
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bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override;
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void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
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void seq_keep(llama_seq_id seq_id) override;
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void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override;
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void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
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llama_pos seq_pos_min(llama_seq_id seq_id) const override;
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llama_pos seq_pos_max(llama_seq_id seq_id) const override;
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std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
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// state write/load
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void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
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void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
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//
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// llama_kv_cache specific API
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//
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uint32_t get_size() const;
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uint32_t get_n_seq_max() const;
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uint32_t get_n_stream() const;
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bool get_has_shift() const;
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ggml_type type_k() const;
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ggml_type type_v() const;
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std::vector<uint32_t> get_layer_ids() const;
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ggml_tensor * get_k_storage(int32_t il) const;
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const llama_kv_cells & get_cells(llama_seq_id seq_id) const;
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// The stream holding seq_id's cells.
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uint32_t get_stream(llama_seq_id seq_id) const;
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// state_read, plus the cells the restored tokens were placed in
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// a cache that mirrors another one (the qwen4exp indexer) must not search for its own cells: two searches agree only by luck
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// sinfos_out: if set, filled with the layout used; a stream with no cells leaves an empty entry
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// sinfos_in : if set, the layout to use instead of searching. one entry per stream, cell count must match the blob
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void state_read_sinfo(
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llama_io_read_i & io,
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llama_seq_id seq_id,
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llama_state_seq_flags flags,
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slot_info_vec_t * sinfos_out,
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const slot_info_vec_t * sinfos_in);
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// undo a state_read() of seq_id (-1 for the whole cache) that another memory module failed to complete
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void state_clear(llama_seq_id seq_id);
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//
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// graph_build API
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//
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uint32_t get_n_kv(const slot_info & sinfo) const;
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// get views of the current state of the cache
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ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
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ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
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// store k_cur and v_cur in the cache based on the provided head location
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ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const;
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ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const;
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//
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// preparation API
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//
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// find places for the provided ubatches in the cache, returns the slot infos
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// return empty vector on failure
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slot_info_vec_t prepare(const std::vector<llama_ubatch> & ubatches);
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bool update(llama_context * lctx, bool do_shift, const stream_copy_info & sc_info);
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// find a slot of kv cells that can hold the ubatch
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// if cont == true, then the slot must be continuous
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// return empty slot_info on failure
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slot_info find_slot(const llama_ubatch & ubatch, bool cont) const;
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// emplace the ubatch context into slot: [sinfo.idxs[0...ubatch.n_tokens - 1]]
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void apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch);
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//
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// input API
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//
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ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
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ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
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ggml_tensor * build_input_k_rot(ggml_context * ctx) const;
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ggml_tensor * build_input_v_rot(ggml_context * ctx) const;
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void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
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void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
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void set_input_k_shift(ggml_tensor * dst) const;
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void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
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void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
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void set_input_k_rot(ggml_tensor * dst) const;
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void set_input_v_rot(ggml_tensor * dst) const;
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// true if llama_kv_cell_ext holds information that has to survive a state save/restore
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bool has_cell_ext() const;
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// for every token of the ubatch, the ids of the n tokens that precede it in its sequence
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// example for M-RoPE image case: tokens A B X X X C, where X is a 3-token image at pos 2 spanning positions 2..4:
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// tok: A B X X X C
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// pos: 0 1 2 2 2 5
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// prev, n=2: A -> [NULL, NULL], B -> [NULL, A], 3rd X -> [X, X], C -> [X, X]
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// note: used by n-gram input embeddings
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void get_prev_tokens(const llama_ubatch & ubatch, uint32_t n, std::vector<llama_token> & res) const;
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private:
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const llama_model & model;
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const llama_hparams & hparams;
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struct kv_layer {
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// layer index in the model
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// note: can be different from the layer index in the KV cache
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uint32_t il;
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ggml_tensor * k;
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ggml_tensor * v;
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std::vector<ggml_tensor *> k_stream;
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std::vector<ggml_tensor *> v_stream;
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};
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bool v_trans = true; // the value tensor is transposed
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const uint32_t n_seq_max = 1;
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const uint32_t n_stream = 1;
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// required padding
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const uint32_t n_pad = 1;
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// SWA
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const uint32_t n_swa = 0;
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// env: LLAMA_ATTN_ROT_DISABLE
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uint32_t n_rot_k = 0;
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uint32_t n_rot_v = 0;
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// if all layers participating in the cache have constant head size, the value is stored here
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// otherwise the value is -1
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int32_t n_embd_head_k_all = 0;
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int32_t n_embd_head_v_all = 0;
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// pre-computed hadamard martrices
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std::unordered_map<int64_t, std::vector<float>> attn_rot_hadamard;
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// env: LLAMA_KV_CACHE_DEBUG
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int debug = 0;
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// this is the SWA type of the cache - not to be confused with the model SWA type
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const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
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// ggml contexts for the KV cache along with the allocated backend buffers:
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llama_memory_bufs ctxs_bufs;
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// the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())
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// note: this is not part of the KV state and it's only used to speed-up the find_slot() method
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std::vector<uint32_t> v_heads;
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// TODO: temporary until we refactor to be able to share the same cells between 2 kv caches [TAG_KV_CACHE_SHARE_CELLS]
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llama_kv_cache * other;
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std::shared_ptr<llama_kv_cells_vec> v_cells_impl;
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llama_kv_cells_vec & v_cells;
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// maps from a sequence id to a stream id
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std::vector<uint32_t> seq_to_stream;
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// pending stream copies that will be applied during the next update
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stream_copy_info sc_info;
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std::vector<kv_layer> layers;
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// model layer id -> KV cache layer id
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std::unordered_map<int32_t, int32_t> map_layer_ids;
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size_t total_size() const;
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size_t size_k_bytes() const;
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size_t size_v_bytes() const;
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ggml_tensor * build_rope_shift(
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const llama_cparams & cparams,
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ggml_context * ctx,
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ggml_tensor * cur,
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ggml_tensor * shift,
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ggml_tensor * rot,
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ggml_tensor * factors,
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float freq_base,
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float freq_scale,
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uint32_t il) const;
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ggml_cgraph * build_graph_shift(
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llm_graph_result * res,
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llama_context * lctx) const;
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struct cell_ranges_t {
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uint32_t strm;
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std::vector<std::pair<uint32_t, uint32_t>> data; // ranges, from inclusive, to exclusive
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};
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void state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id = -1) const;
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void state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const;
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// sinfo_in, when set, replaces the find_slot call: the cells are given by the caller
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bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id = -1, const slot_info * sinfo_in = nullptr);
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bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo);
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void state_clear(llama_seq_id seq_id, uint32_t strm, const slot_info & sinfo);
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};
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class llama_kv_cache_context : public llama_memory_context_i {
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public:
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// some shorthands
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using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
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using stream_copy_info = llama_kv_cache::stream_copy_info;
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// used for errors
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llama_kv_cache_context(llama_memory_status status);
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// used to create a full-cache context
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llama_kv_cache_context(
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llama_kv_cache * kv);
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// used to create an update context
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llama_kv_cache_context(
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llama_kv_cache * kv,
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llama_context * lctx,
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bool do_shift,
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stream_copy_info sc_info);
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// used to create a batch processing context from a batch
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llama_kv_cache_context(
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llama_kv_cache * kv,
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slot_info_vec_t sinfos,
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std::vector<llama_ubatch> ubatches);
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virtual ~llama_kv_cache_context();
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//
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// llama_memory_context_i
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//
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bool next() override;
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bool apply() override;
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llama_memory_status get_status() const override;
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const llama_ubatch & get_ubatch() const override;
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//
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// llama_kv_cache_context specific API
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//
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uint32_t get_n_kv() const;
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ggml_type type_k() const;
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ggml_type type_v() const;
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// get views of the current state of the cache
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ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
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ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
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// store k_cur and v_cur in the cache based on the provided head location
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// note: the heads in k_cur and v_cur should be laid out contiguously in memory
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// - k_cur [n_embd_head_k, n_head_k, n_tokens]
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// - k_idxs [n_tokens]
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// - v_cur [n_embd_head_v, n_head_v, n_tokens]
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// - v_idxs [n_tokens] or [n_tokens*n_embd_v_gqa] depending if V cache is transposed
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ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;
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ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const;
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// create destination indices for each head of the current batch for where it would be written in the KV cache
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// the indices address the global KV cache (not per stream) - this is not relevant for the user of this API, but
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// helps understand the implementation logic of cpy_k and cpy_v
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ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
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ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
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ggml_tensor * build_input_k_rot(ggml_context * ctx) const;
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ggml_tensor * build_input_v_rot(ggml_context * ctx) const;
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void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;
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void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;
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||||
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||||
void set_input_k_shift (ggml_tensor * dst) const;
|
||||
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
|
||||
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
|
||||
|
||||
void set_input_k_rot(ggml_tensor * dst) const;
|
||||
void set_input_v_rot(ggml_tensor * dst) const;
|
||||
|
||||
// see llama_kv_cache::get_prev_tokens()
|
||||
void get_prev_tokens(const llama_ubatch & ubatch, uint32_t n, std::vector<llama_token> & res) const;
|
||||
|
||||
private:
|
||||
llama_memory_status status;
|
||||
|
||||
llama_kv_cache * kv;
|
||||
llama_context * lctx;
|
||||
|
||||
//
|
||||
// update context
|
||||
//
|
||||
|
||||
bool do_shift = false;
|
||||
|
||||
stream_copy_info sc_info;
|
||||
|
||||
//
|
||||
// batch processing context
|
||||
//
|
||||
|
||||
// the index of the cur ubatch to process
|
||||
size_t i_cur = 0;
|
||||
|
||||
slot_info_vec_t sinfos;
|
||||
|
||||
std::vector<llama_ubatch> ubatches;
|
||||
|
||||
//
|
||||
// data needed for building the compute graph for the current ubatch:
|
||||
//
|
||||
|
||||
// a heuristic, to avoid attending the full cache if it is not yet utilized
|
||||
// as the cache gets filled, the benefit from this heuristic disappears
|
||||
int32_t n_kv;
|
||||
};
|
||||
Reference in new issue
Block a user