whisper.cpp/examples/talk-llama/llama-kv-cache-unified-iswa.cpp
Georgi Gerganov 7fd6fa8097 talk-llama : sync llama.cpp
ggml-ci
2025-06-01 15:14:44 +03:00

250 lines
8.1 KiB
C++

#include "llama-kv-cache-unified-iswa.h"
#include "llama-impl.h"
#include "llama-batch.h"
#include "llama-model.h"
#include <algorithm>
#include <cassert>
//
// llama_kv_cache_unified_iswa
//
llama_kv_cache_unified_iswa::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) : hparams(model.hparams) {
llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
llama_kv_cache_unified::layer_filter_cb filter_swa = [&](int32_t il) { return model.hparams.is_swa(il); };
const uint32_t size_base = kv_size;
uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_ubatch, n_pad));
// when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
if (swa_full) {
LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n",
__func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
size_swa = size_base;
}
LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
kv_base = std::make_unique<llama_kv_cache_unified>(
model, std::move(filter_base), type_k, type_v,
v_trans, offload, size_base, n_seq_max, n_pad,
0, LLAMA_SWA_TYPE_NONE);
LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
kv_swa = std::make_unique<llama_kv_cache_unified>(
model, std::move(filter_swa), type_k, type_v,
v_trans, offload, size_swa, n_seq_max, n_pad,
hparams.n_swa, hparams.swa_type);
}
void llama_kv_cache_unified_iswa::clear() {
kv_base->clear();
kv_swa ->clear();
}
bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
bool res = true;
res = res & kv_base->seq_rm(seq_id, p0, p1);
res = res & kv_swa ->seq_rm(seq_id, p0, p1);
return res;
}
void llama_kv_cache_unified_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1);
kv_swa ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
}
void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) {
kv_base->seq_keep(seq_id);
kv_swa ->seq_keep(seq_id);
}
void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
kv_base->seq_add(seq_id, p0, p1, shift);
kv_swa ->seq_add(seq_id, p0, p1, shift);
}
void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
kv_base->seq_div(seq_id, p0, p1, d);
kv_swa ->seq_div(seq_id, p0, p1, d);
}
llama_pos llama_kv_cache_unified_iswa::seq_pos_min(llama_seq_id seq_id) const {
// the base cache is a superset of the SWA cache, so we can just check the SWA cache
return kv_swa->seq_pos_min(seq_id);
}
llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
return kv_swa->seq_pos_max(seq_id);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
GGML_UNUSED(embd_pooled);
// TODO: if we fail with split_simple, we should attempt different splitting strategies
// but to do that properly, we first have to refactor the batches to be more flexible
auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
std::vector<llama_ubatch> ubatches;
while (sbatch.n_tokens > 0) {
auto ubatch = sbatch.split_simple(n_ubatch);
ubatches.push_back(ubatch);
}
auto heads_base = kv_base->prepare(ubatches);
if (heads_base.empty()) {
return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
auto heads_swa = kv_swa->prepare(ubatches);
if (heads_swa.empty()) {
return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
assert(heads_base.size() == heads_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_SUCCESS,
this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {
return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_SUCCESS, this);
}
bool llama_kv_cache_unified_iswa::update(llama_context & lctx) {
bool res = false;
res = res | kv_base->update(lctx);
res = res | kv_swa ->update(lctx);
return res;
}
void llama_kv_cache_unified_iswa::defrag_sched(float thold) {
kv_base->defrag_sched(thold);
kv_swa ->defrag_sched(thold);
}
bool llama_kv_cache_unified_iswa::get_can_shift() const {
return kv_base->get_size() == kv_swa->get_size();
}
void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
kv_base->state_write(io, seq_id);
kv_swa ->state_write(io, seq_id);
}
void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
kv_base->state_read(io, seq_id);
kv_swa ->state_read(io, seq_id);
}
llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_base() const {
return kv_base.get();
}
llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const {
return kv_swa.get();
}
//
// llama_kv_cache_unified_iswa_state
//
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_memory_status status,
llama_kv_cache_unified_iswa * kv) : status(status) {
state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base()));
state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa ()));
}
llama_kv_cache_unified_iswa_state::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)
: status(status),
sbatch(std::move(sbatch)),
ubatches(std::move(ubatches)) {
// note: here we copy the ubatches. not sure if this is ideal
state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base(), {}, std::move(heads_base), this->ubatches));
state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa (), {}, std::move(heads_swa), this->ubatches));
}
llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default;
bool llama_kv_cache_unified_iswa_state::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
state_base->next();
state_swa ->next();
if (++i_next >= ubatches.size()) {
return false;
}
return true;
}
bool llama_kv_cache_unified_iswa_state::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
bool res = true;
res = res & state_base->apply();
res = res & state_swa ->apply();
return res;
}
std::vector<int64_t> & llama_kv_cache_unified_iswa_state::out_ids() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return sbatch.out_ids;
}
llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return state_base.get();
}
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return state_swa.get();
}