3600cc2886
* llama : use n_swa + n_ubatch cells for SWA cache ggml-ci * llama : add warning about multi-sqeuence SWA contexts
636 lines
19 KiB
C++
636 lines
19 KiB
C++
#pragma once
|
|
|
|
#include "llama.h"
|
|
#include "llama-io.h"
|
|
#include "llama-batch.h"
|
|
#include "llama-graph.h"
|
|
#include "llama-memory.h"
|
|
#include "llama-kv-cells.h"
|
|
|
|
#include "ggml-cpp.h"
|
|
|
|
#include <set>
|
|
#include <unordered_map>
|
|
#include <vector>
|
|
|
|
struct llama_cparams;
|
|
struct llama_hparams;
|
|
struct llama_model;
|
|
struct llama_context;
|
|
|
|
struct llama_kv_cache : public llama_memory_i {
|
|
virtual ~llama_kv_cache() = default;
|
|
|
|
// split the input batch into a set of ubatches and verify that they can fit into the cache
|
|
// return a state object containing the ubatches and KV cache state required to process them
|
|
// check the llama_memory_state_i::get_status() for the result
|
|
virtual llama_memory_state_ptr init_batch(
|
|
const llama_batch & batch,
|
|
uint32_t n_ubatch,
|
|
bool embd_pooled,
|
|
bool logits_all) = 0;
|
|
|
|
// simulate full cache, used for allocating worst-case compute buffers
|
|
virtual llama_memory_state_ptr init_full() = 0;
|
|
|
|
// process any pending defrag/shift/etc. operations
|
|
// optionally call once before processing a new batch
|
|
// return true if any operations were performed
|
|
virtual bool update(llama_context & lctx) = 0;
|
|
|
|
// schedule a defrag if the fragmentation threshold is exceeded. otherwise, do nothing
|
|
// TODO: change to
|
|
// llama_memory_state_ptr init_defrag(float thold) = 0;
|
|
//
|
|
virtual void defrag_sched(float thold) = 0;
|
|
|
|
// getters
|
|
virtual bool get_can_shift() const = 0;
|
|
|
|
bool get_can_edit() const override { return get_can_shift(); }
|
|
|
|
//
|
|
// state write/read
|
|
//
|
|
|
|
virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
|
|
virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0;
|
|
};
|
|
|
|
//
|
|
// llama_kv_cache_unified
|
|
//
|
|
|
|
class llama_kv_cache_unified : public llama_kv_cache {
|
|
public:
|
|
static uint32_t get_padding(const llama_cparams & cparams);
|
|
|
|
// 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_kv_cache_unified(
|
|
const llama_model & model,
|
|
layer_filter_cb && filter,
|
|
ggml_type type_k,
|
|
ggml_type type_v,
|
|
bool v_trans,
|
|
bool offload,
|
|
uint32_t kv_size,
|
|
uint32_t n_seq_max,
|
|
uint32_t n_pad,
|
|
uint32_t n_swa,
|
|
llama_swa_type swa_type);
|
|
|
|
~llama_kv_cache_unified() = default;
|
|
|
|
//
|
|
// llama_memory_i
|
|
//
|
|
|
|
void clear() 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;
|
|
|
|
//
|
|
// llama_kv_cache
|
|
//
|
|
|
|
llama_memory_state_ptr init_batch(
|
|
const llama_batch & batch,
|
|
uint32_t n_ubatch,
|
|
bool embd_pooled,
|
|
bool logits_all) override;
|
|
|
|
llama_memory_state_ptr init_full() override;
|
|
|
|
bool update(llama_context & lctx) override;
|
|
|
|
void defrag_sched(float thold) override;
|
|
|
|
bool get_can_shift() const override;
|
|
|
|
// state write/load
|
|
|
|
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
|
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
|
|
|
//
|
|
// llama_kv_cache_unified specific API
|
|
//
|
|
|
|
uint32_t get_size() const;
|
|
|
|
//
|
|
// graph_build API
|
|
//
|
|
|
|
uint32_t get_n_kv() const;
|
|
|
|
// get views of the current state of the cache
|
|
ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
|
|
ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
|
|
|
|
// store k_cur and v_cur in the cache based on the provided head location
|
|
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
|
|
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
|
|
|
|
//
|
|
// preparation API
|
|
//
|
|
|
|
// find places for the provided ubatches in the cache, returns the head locations
|
|
// return empty vector on failure
|
|
std::vector<uint32_t> prepare(const std::vector<llama_ubatch> & ubatches);
|
|
|
|
// return the cell position where we can insert the ubatch
|
|
// return -1 on failure to find a contiguous slot of kv cells
|
|
int32_t find_slot(const llama_ubatch & ubatch) const;
|
|
|
|
// emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
|
|
void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
|
|
|
|
//
|
|
// set_input API
|
|
//
|
|
|
|
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
|
|
void set_input_k_shift (ggml_tensor * dst) const;
|
|
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
|
|
|
|
private:
|
|
const llama_model & model;
|
|
const llama_hparams & hparams;
|
|
|
|
struct kv_layer {
|
|
// layer index in the model
|
|
// note: can be different from the layer index in the KV cache
|
|
uint32_t il;
|
|
|
|
ggml_tensor * k;
|
|
ggml_tensor * v;
|
|
};
|
|
|
|
bool do_defrag = false;
|
|
bool v_trans = true; // the value tensor is transposed
|
|
|
|
// the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())
|
|
// note: this is not part of the KV state and it's only used to speed-up the find_slot() method
|
|
uint32_t head = 0;
|
|
|
|
const uint32_t n_seq_max = 1;
|
|
|
|
// required padding
|
|
const uint32_t n_pad = 1;
|
|
|
|
// SWA
|
|
const uint32_t n_swa = 0;
|
|
|
|
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
|
|
|
|
std::vector<ggml_context_ptr> ctxs;
|
|
std::vector<ggml_backend_buffer_ptr> bufs;
|
|
|
|
llama_kv_cells_unified cells;
|
|
|
|
std::vector<kv_layer> layers;
|
|
|
|
// model layer id -> KV cache layer id
|
|
std::unordered_map<int32_t, int32_t> map_layer_ids;
|
|
|
|
// defrag
|
|
struct {
|
|
std::vector<uint32_t> ids;
|
|
} defrag_info;
|
|
|
|
// return true if cells have been moved
|
|
bool defrag_prepare(int32_t n_max_nodes);
|
|
|
|
size_t total_size() const;
|
|
|
|
size_t size_k_bytes() const;
|
|
size_t size_v_bytes() const;
|
|
|
|
bool is_masked_swa(llama_pos p0, llama_pos p1) const;
|
|
|
|
ggml_tensor * build_rope_shift(
|
|
const llama_cparams & cparams,
|
|
ggml_context * ctx,
|
|
ggml_tensor * cur,
|
|
ggml_tensor * shift,
|
|
ggml_tensor * factors,
|
|
float freq_base,
|
|
float freq_scale) const;
|
|
|
|
llm_graph_result_ptr build_graph_shift(
|
|
const llama_cparams & cparams,
|
|
ggml_context * ctx,
|
|
ggml_cgraph * gf) const;
|
|
|
|
llm_graph_result_ptr build_graph_defrag(
|
|
const llama_cparams & cparams,
|
|
ggml_context * ctx,
|
|
ggml_cgraph * gf) const;
|
|
|
|
void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
|
|
void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
|
|
|
|
bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
|
|
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
|
|
};
|
|
|
|
class llama_kv_cache_unified_state : public llama_memory_state_i {
|
|
public:
|
|
// used for errors
|
|
llama_kv_cache_unified_state(llama_memory_status status);
|
|
|
|
// used to create a full-cache state
|
|
llama_kv_cache_unified_state(
|
|
llama_memory_status status,
|
|
llama_kv_cache_unified * kv);
|
|
|
|
// used to create a state from a batch
|
|
llama_kv_cache_unified_state(
|
|
llama_memory_status status,
|
|
llama_kv_cache_unified * kv,
|
|
llama_sbatch sbatch,
|
|
std::vector<uint32_t> heads,
|
|
std::vector<llama_ubatch> ubatches);
|
|
|
|
virtual ~llama_kv_cache_unified_state();
|
|
|
|
//
|
|
// llama_memory_state_i
|
|
//
|
|
|
|
bool next() override;
|
|
bool apply() override;
|
|
|
|
std::vector<int64_t> & out_ids() override;
|
|
|
|
llama_memory_status get_status() const override;
|
|
const llama_ubatch & get_ubatch() const override;
|
|
|
|
//
|
|
// llama_kv_cache_unified_state specific API
|
|
//
|
|
|
|
uint32_t get_n_kv() const;
|
|
|
|
// get views of the current state of the cache
|
|
ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
|
|
ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
|
|
|
|
// store k_cur and v_cur in the cache based on the provided head location
|
|
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
|
|
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
|
|
|
|
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;
|
|
|
|
private:
|
|
const llama_memory_status status;
|
|
|
|
llama_kv_cache_unified * kv;
|
|
|
|
llama_sbatch sbatch;
|
|
|
|
// the index of the next ubatch to process
|
|
size_t i_next = 0;
|
|
|
|
std::vector<uint32_t> heads;
|
|
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;
|
|
|
|
// the beginning of the current slot in which the ubatch will be inserted
|
|
int32_t head;
|
|
};
|
|
|
|
//
|
|
// llama_kv_cache_unified_iswa
|
|
//
|
|
|
|
// utilizes two instances of llama_kv_cache_unified
|
|
// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
|
|
|
|
class llama_kv_cache_unified_iswa : public llama_kv_cache {
|
|
public:
|
|
llama_kv_cache_unified_iswa(
|
|
const llama_model & model,
|
|
ggml_type type_k,
|
|
ggml_type type_v,
|
|
bool v_trans,
|
|
bool offload,
|
|
bool swa_full,
|
|
uint32_t kv_size,
|
|
uint32_t n_seq_max,
|
|
uint32_t n_ubatch,
|
|
uint32_t n_pad);
|
|
|
|
~llama_kv_cache_unified_iswa() = default;
|
|
|
|
//
|
|
// llama_memory_i
|
|
//
|
|
|
|
void clear() 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;
|
|
|
|
//
|
|
// llama_kv_cache
|
|
//
|
|
|
|
llama_memory_state_ptr init_batch(
|
|
const llama_batch & batch,
|
|
uint32_t n_ubatch,
|
|
bool embd_pooled,
|
|
bool logits_all) override;
|
|
|
|
llama_memory_state_ptr init_full() override;
|
|
|
|
bool update(llama_context & lctx) override;
|
|
|
|
void defrag_sched(float thold) override;
|
|
|
|
bool get_can_shift() const override;
|
|
|
|
// state write/load
|
|
|
|
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
|
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
|
|
|
//
|
|
// llama_kv_cache_unified_iswa specific API
|
|
//
|
|
|
|
llama_kv_cache_unified * get_base() const;
|
|
llama_kv_cache_unified * get_swa () const;
|
|
|
|
private:
|
|
const llama_hparams & hparams;
|
|
|
|
std::unique_ptr<llama_kv_cache_unified> kv_base;
|
|
std::unique_ptr<llama_kv_cache_unified> kv_swa;
|
|
};
|
|
|
|
class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
|
|
public:
|
|
// used for errors
|
|
llama_kv_cache_unified_iswa_state(llama_memory_status status);
|
|
|
|
// used to create a full-cache state
|
|
llama_kv_cache_unified_iswa_state(
|
|
llama_memory_status status,
|
|
llama_kv_cache_unified_iswa * kv);
|
|
|
|
// used to create a state from a batch
|
|
llama_kv_cache_unified_iswa_state(
|
|
llama_memory_status status,
|
|
llama_kv_cache_unified_iswa * kv,
|
|
llama_sbatch sbatch,
|
|
std::vector<uint32_t> heads_base,
|
|
std::vector<uint32_t> heads_swa,
|
|
std::vector<llama_ubatch> ubatches);
|
|
|
|
virtual ~llama_kv_cache_unified_iswa_state();
|
|
|
|
//
|
|
// llama_memory_state_i
|
|
//
|
|
|
|
bool next() override;
|
|
bool apply() override;
|
|
|
|
std::vector<int64_t> & out_ids() override;
|
|
|
|
llama_memory_status get_status() const override;
|
|
const llama_ubatch & get_ubatch() const override;
|
|
|
|
//
|
|
// llama_kv_cache_unified_iswa_state specific API
|
|
//
|
|
|
|
const llama_kv_cache_unified_state * get_base() const;
|
|
const llama_kv_cache_unified_state * get_swa() const;
|
|
|
|
private:
|
|
const llama_memory_status status;
|
|
|
|
//llama_kv_cache_unified_iswa * kv;
|
|
|
|
llama_sbatch sbatch;
|
|
|
|
// the index of the next ubatch to process
|
|
size_t i_next = 0;
|
|
|
|
std::vector<llama_ubatch> ubatches;
|
|
|
|
std::unique_ptr<llama_kv_cache_unified_state> state_base;
|
|
std::unique_ptr<llama_kv_cache_unified_state> state_swa;
|
|
};
|
|
|
|
//
|
|
// llama_kv_cache_recurrent
|
|
//
|
|
|
|
// TODO: extract the KV cache state used for graph computation into llama_kv_cache_recurrent_state_i
|
|
// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
|
|
class llama_kv_cache_recurrent : public llama_kv_cache {
|
|
public:
|
|
llama_kv_cache_recurrent(
|
|
const llama_model & model,
|
|
ggml_type type_k,
|
|
ggml_type type_v,
|
|
bool offload,
|
|
uint32_t kv_size,
|
|
uint32_t n_seq_max);
|
|
|
|
~llama_kv_cache_recurrent() = default;
|
|
|
|
//
|
|
// llama_memory_i
|
|
//
|
|
|
|
void clear() 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;
|
|
|
|
//
|
|
// llama_kv_cache
|
|
//
|
|
|
|
llama_memory_state_ptr init_batch(
|
|
const llama_batch & batch,
|
|
uint32_t n_ubatch,
|
|
bool embd_pooled,
|
|
bool logits_all) override;
|
|
|
|
llama_memory_state_ptr init_full() override;
|
|
|
|
bool update(llama_context & lctx) override;
|
|
|
|
void defrag_sched(float thold) override;
|
|
|
|
bool prepare(const std::vector<llama_ubatch> & ubatches);
|
|
|
|
// find a contiguous slot of kv cells and emplace the ubatch there
|
|
bool find_slot(const llama_ubatch & ubatch);
|
|
|
|
bool get_can_shift() const override;
|
|
|
|
// TODO: temporary methods - they are not really const as they do const_cast<>, fix this
|
|
int32_t s_copy(int i) const;
|
|
float s_mask(int i) const;
|
|
|
|
// state write/load
|
|
|
|
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
|
|
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
|
|
|
|
uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
|
|
uint32_t size = 0; // total number of cells, shared across all sequences
|
|
uint32_t used = 0; // used cells (i.e. at least one seq_id)
|
|
|
|
// computed before each graph build
|
|
uint32_t n = 0;
|
|
|
|
// TODO: optimize for recurrent state needs
|
|
struct kv_cell {
|
|
llama_pos pos = -1;
|
|
int32_t src = -1; // used to copy states
|
|
int32_t tail = -1;
|
|
|
|
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 kv_cell & other) const {
|
|
return seq_id == other.seq_id;
|
|
}
|
|
};
|
|
|
|
std::vector<kv_cell> cells;
|
|
|
|
std::vector<ggml_tensor *> k_l; // per layer
|
|
std::vector<ggml_tensor *> v_l;
|
|
|
|
private:
|
|
//const llama_model & model;
|
|
const llama_hparams & hparams;
|
|
|
|
const uint32_t n_seq_max = 1;
|
|
|
|
std::vector<ggml_context_ptr> ctxs;
|
|
std::vector<ggml_backend_buffer_ptr> bufs;
|
|
|
|
size_t total_size() const;
|
|
|
|
size_t size_k_bytes() const;
|
|
size_t size_v_bytes() const;
|
|
|
|
void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
|
|
void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
|
|
|
|
bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
|
|
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
|
|
};
|
|
|
|
class llama_kv_cache_recurrent_state : public llama_memory_state_i {
|
|
public:
|
|
// used for errors
|
|
llama_kv_cache_recurrent_state(llama_memory_status status);
|
|
|
|
// used to create a full-cache state
|
|
llama_kv_cache_recurrent_state(
|
|
llama_memory_status status,
|
|
llama_kv_cache_recurrent * kv);
|
|
|
|
// used to create a state from a batch
|
|
llama_kv_cache_recurrent_state(
|
|
llama_memory_status status,
|
|
llama_kv_cache_recurrent * kv,
|
|
llama_sbatch sbatch,
|
|
std::vector<llama_ubatch> ubatches);
|
|
|
|
virtual ~llama_kv_cache_recurrent_state();
|
|
|
|
//
|
|
// llama_memory_state_i
|
|
//
|
|
|
|
bool next() override;
|
|
bool apply() override;
|
|
|
|
std::vector<int64_t> & out_ids() override;
|
|
|
|
llama_memory_status get_status() const override;
|
|
const llama_ubatch & get_ubatch() const override;
|
|
|
|
//
|
|
// llama_kv_cache_recurrent_state specific API
|
|
//
|
|
|
|
uint32_t get_n_kv() const;
|
|
uint32_t get_head() const;
|
|
uint32_t get_size() const;
|
|
|
|
ggml_tensor * get_k_l(int32_t il) const;
|
|
ggml_tensor * get_v_l(int32_t il) const;
|
|
|
|
int32_t s_copy(int i) const;
|
|
float s_mask(int i) const;
|
|
|
|
private:
|
|
const llama_memory_status status;
|
|
|
|
llama_kv_cache_recurrent * kv;
|
|
|
|
llama_sbatch sbatch;
|
|
|
|
size_t i_next = 0;
|
|
|
|
std::vector<llama_ubatch> ubatches;
|
|
|
|
//
|
|
// data needed for building the compute graph for the current ubatch:
|
|
// TODO: extract all the state like `head` and `n` here
|
|
//
|
|
|
|
const bool is_full = false;
|
|
};
|