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llama: update to commit 71e90e88 (#10192)

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This commit is contained in:
Jeffrey Morgan
2025-04-16 18:14:01 -04:00
committed by GitHub
parent 369de832cd
commit 943464ccb8
160 changed files with 42219 additions and 33080 deletions
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318
llama/llama.cpp/src/llama-kv-cache.h vendored
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@@ -1,15 +1,58 @@
#pragma once
#include "llama.h"
#include "llama-io.h"
#include "llama-memory.h"
#include "ggml-cpp.h"
#include <functional>
#include <set>
#include <vector>
struct llama_cparams;
struct llama_hparams;
struct llama_ubatch;
struct llama_kv_cache : public llama_memory_i {
using llama_memory_i::llama_memory_i;
virtual void restore() = 0; // call if batch processing fails - restores the cache state
virtual void commit() = 0; // call after successful batch processing - clears any pending state
virtual int32_t get_n_tokens() const = 0;
virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache
virtual bool get_can_shift() const = 0;
bool get_can_edit() const override { return get_can_shift(); }
};
struct llama_kv_cache_guard {
llama_kv_cache_guard(llama_kv_cache * kv) : kv(kv) {}
~llama_kv_cache_guard() {
kv->restore();
}
void commit() {
kv->commit();
}
private:
llama_kv_cache * kv;
};
// block of KV slots to move when defragging
struct llama_kv_defrag_move {
uint32_t src;
uint32_t dst;
uint32_t len;
};
struct llama_kv_cell {
llama_pos pos = -1;
llama_pos delta = 0;
llama_pos delta = 0;
int32_t src = -1; // used by recurrent state models to copy states
int32_t tail = -1;
@@ -29,10 +72,107 @@ struct llama_kv_cell {
};
// ring-buffer of cached KV data
struct llama_kv_cache {
// TODO: pimpl
// TODO: add notion of max sequences
class llama_kv_cache_unified : public llama_kv_cache {
public:
// can be used to query data from the model if needed
struct callbacks {
std::function<ggml_tensor * (uint32_t n_ctx_per_seq, int il)> get_rope_factors;
};
llama_kv_cache_unified(
const llama_hparams & hparams,
callbacks cbs);
virtual ~llama_kv_cache_unified() = default;
// TODO: become constructor
bool init(
const llama_model & model, // TODO: do not reference the model
const llama_cparams & cparams,
ggml_type type_k,
ggml_type type_v,
uint32_t kv_size,
bool offload);
int32_t get_n_tokens() const override;
int32_t get_used_cells() const override;
size_t total_size() const;
// TODO: better data structures to reduce the cost of this operation
llama_pos pos_max() const;
void clear() override;
void defrag() override;
virtual void restore() override;
virtual void commit() 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 delta) override;
void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
bool get_can_shift() const override;
// find an empty slot of size "n_tokens" in the cache
// updates the cache head
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
bool find_slot(const llama_ubatch & batch);
// TODO: maybe not needed
uint32_t get_padding(const llama_cparams & cparams) const;
// find how many cells are currently in use
uint32_t cell_max() const;
size_t size_k_bytes() const;
size_t size_v_bytes() const;
// defrag
struct {
std::vector<llama_kv_defrag_move> moves;
} defrag_info;
// return true if cells have been moved
bool defrag_prepare(int32_t n_max_nodes);
// commit/restore cache
struct slot_range {
uint32_t c0 = 0; // note: these are cell indices, not sequence positions
uint32_t c1 = 0;
};
// pending cell updates that are not yet committed
struct {
std::vector<slot_range> ranges;
} pending;
// state write/load
void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const;
void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1);
// members
const llama_hparams & hparams;
callbacks cbs;
bool has_shift = false;
bool do_defrag = false;
// TODO: remove this and implement llama_kv_cache_recurrent instead
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
bool can_shift = false;
@@ -46,173 +186,35 @@ struct llama_kv_cache {
// computed before each graph build
uint32_t n = 0;
std::vector<llama_kv_cell> cells;
std::vector<ggml_tensor *> k_l; // per layer
std::vector<ggml_tensor *> v_l;
private:
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<ggml_context_ptr> ctxs;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
size_t total_size() const {
size_t size = 0;
for (const auto & buf : bufs) {
size += ggml_backend_buffer_get_size(buf.get());
}
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;
return size;
}
// TODO: better data structures to reduce the cost of this operation
llama_pos max_pos() const {
llama_pos max_pos = -1;
for (const auto & cell : cells) {
max_pos = std::max(max_pos, cell.pos);
}
return max_pos;
}
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);
};
// a structure holds information about the slot found in llama_kv_cache_find_slot
struct llama_kv_cache_slot_info {
std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
bool found = false; // the slot was found
explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
operator bool() const { return found; }
};
// TODO: maybe not needed
uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams);
bool llama_kv_cache_init(
struct llama_kv_cache & cache,
const llama_model & model,
const llama_cparams & cparams,
ggml_type type_k,
ggml_type type_v,
uint32_t kv_size,
bool offload);
// find an empty slot of size "n_tokens" in the cache
// updates the cache head
// returns a structure holding information about the slot found
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
struct llama_kv_cache & cache,
const struct llama_ubatch & batch);
// find how many cells are currently in use
uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache);
void llama_kv_cache_clear(struct llama_kv_cache & cache);
bool llama_kv_cache_seq_rm(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1);
void llama_kv_cache_seq_cp(
struct llama_kv_cache & cache,
llama_seq_id seq_id_src,
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1);
void llama_kv_cache_seq_keep(
struct llama_kv_cache & cache,
llama_seq_id seq_id);
void llama_kv_cache_seq_add(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
llama_pos delta);
void llama_kv_cache_seq_div(
struct llama_kv_cache & cache,
llama_seq_id seq_id,
llama_pos p0,
llama_pos p1,
int d);
llama_pos llama_kv_cache_seq_pos_max(
struct llama_kv_cache & cache,
llama_seq_id seq_id);
void llama_kv_cache_defrag(struct llama_kv_cache & cache);
int32_t llama_get_kv_cache_token_count(const struct llama_kv_cache & kv);
int32_t llama_get_kv_cache_used_cells(const struct llama_kv_cache & kv);
bool llama_kv_cache_can_shift(const struct llama_kv_cache & kv);
// TODO: temporary reusing llama_kv_cache_unified -- implement recurrent cache and simplify llama_kv_cache_unified
//class llama_kv_cache_recurrent : public llama_kv_cache_unified {
//public:
// using llama_kv_cache_unified::llama_kv_cache_unified;
//};
//
// kv cache view
//
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_kv_cache & kv, int32_t n_seq_max);
void llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_kv_cache & kv);
//
// kv cache restore
//
// saves the kv_cache state for future recovery.
// used to rollback llama_kv_cache_find_slot changes.
struct llama_kv_slot_restorer {
struct llama_kv_cache_state {
uint32_t head = 0;
uint32_t n = 0;
} old_state;
// for non-recurrent models only
// list of slots to restore
std::vector<std::pair<uint32_t, uint32_t>> slot_boundaries;
bool do_restore = false;
explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) {
old_state.head = cache.head;
old_state.n = cache.n;
}
// saves a slot information for future restoration
void save(const struct llama_kv_cache_slot_info & slot) {
if (slot) {
do_restore = true;
if (slot.boundaries.first != slot.boundaries.second) {
slot_boundaries.push_back(slot.boundaries);
}
}
}
// must be explicitly called to restore the kv_cache state
// and rollback changes from all llama_kv_cache_find_slot calls
void restore(struct llama_kv_cache & cache) {
if (do_restore) {
cache.head = old_state.head;
cache.n = old_state.n;
if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased
llama_kv_cache_seq_rm(cache, -1, -1, -1);
} else {
for (auto & slot : slot_boundaries) {
llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second);
}
}
}
}
};
llama_kv_cache_view llama_kv_cache_view_init(const llama_kv_cache & kv, int32_t n_seq_max);
void llama_kv_cache_view_update(llama_kv_cache_view * view, const llama_kv_cache * kv);