vbatts/llama.cpp
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llama : add llama_kv_cache_compress (EXPERIMENTAL)

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Georgi Gerganov 2024-02-25 11:00:19 +02:00
parent 715a343343
commit fdfa5bc76b
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GPG key ID: 449E073F9DC10735
3 changed files with 215 additions and 7 deletions
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1
examples/passkey/passkey.cpp
View file

@ -148,6 +148,7 @@ int main(int argc, char ** argv) {
llama_kv_cache_seq_add (ctx, 0, n_past - n_batch, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
llama_kv_cache_compress(ctx, 0);
llama_kv_cache_update (ctx);
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;

210
llama.cpp
View file

@ -1733,6 +1733,12 @@ struct llama_kv_cache {
// computed before each graph build
uint32_t n = 0;
ggml_type type_k = GGML_TYPE_F16;
ggml_type type_v = GGML_TYPE_F16;
// if non-negative, compress data on next update
llama_pos compress_delta = -1;
std::vector<llama_kv_cell> cells;
std::vector<struct ggml_tensor *> k_l; // per layer
@ -1968,8 +1974,8 @@ struct llama_context {
static bool llama_kv_cache_init(
struct llama_kv_cache & cache,
const llama_model & model,
ggml_type ktype,
ggml_type vtype,
ggml_type type_k,
ggml_type type_v,
uint32_t n_ctx,
bool offload) {
const struct llama_hparams & hparams = model.hparams;
@ -1984,6 +1990,9 @@ static bool llama_kv_cache_init(
cache.size = n_ctx;
cache.used = 0;
cache.type_k = type_k;
cache.type_v = type_v;
cache.cells.clear();
cache.cells.resize(n_ctx);
@ -2024,8 +2033,8 @@ static bool llama_kv_cache_init(
for (int i = 0; i < (int) n_layer; i++) {
struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
ggml_tensor * k = ggml_new_tensor_1d(ctx, ktype, n_embd_k_gqa*n_ctx);
ggml_tensor * v = ggml_new_tensor_1d(ctx, vtype, n_embd_v_gqa*n_ctx);
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*n_ctx);
ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*n_ctx);
ggml_format_name(k, "cache_k_l%d", i);
ggml_format_name(v, "cache_v_l%d", i);
cache.k_l.push_back(k);
@ -2265,6 +2274,10 @@ static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama
return result;
}
static void llama_kv_cache_compress(struct llama_kv_cache & cache, llama_pos delta) {
cache.compress_delta = delta;
}
//
// model loading and saving
//
@ -8034,6 +8047,191 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) {
}
}
}
// compress the KV cache data if needed:
//
// - determine which KV cell pairs (i0, i1) to merge:
//
// abs(cell[i0].pos - cell[i1].pos) <= compress_delta
//
// - move the KV cache to the Host memory for easier maniiplation
// - processing is done layer-by-layer
// - convert the KV data to F32
// - merge the KV data (different ways to merge)
// - convert the KV data back to the original type
// - move the KV cache back to the device memory
// - update the KV cache metadata
//
// as a side effect, the new KV cache is defragmented
//
if (lctx.kv_self.compress_delta >= 0) {
auto & kv_self = lctx.kv_self;
const auto & hparams = lctx.model.hparams;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
const uint32_t kv_size = kv_self.size;
std::vector<uint8_t> buf_q;
std::vector<float> buf_src_f32;
std::vector<float> buf_dst_f32;
const int64_t t_start = ggml_time_us();
struct c_pair { uint32_t i0, i1; };
struct c_info { bool merged; uint32_t id, cnt;};
std::vector<c_info> infos(kv_size, { false, 0, 0 });
// the destination cell in the new KV cache
uint32_t id = 0;
// number of pairs merged
uint32_t n_merges = 0;
// determine which KV cells to merge
for (uint32_t i0 = 0; i0 < kv_size; ++i0) {
const auto & cell0 = kv_self.cells[i0];
if (!cell0.is_empty() && !infos[i0].merged) {
infos[i0] = { true, id, 0 };
infos[id].cnt = 1;
const llama_pos p0 = cell0.pos;
for (uint32_t i1 = i0 + 1; i1 < kv_size; ++i1) {
const auto & cell1 = kv_self.cells[i1];
if (i0 != i1 && cell0.is_same_seq(cell1)) {
const llama_pos p1 = cell1.pos;
if (std::abs(p0 - p1) <= kv_self.compress_delta) {
infos[i1] = { true, id, 0 };
infos[id].cnt++;
n_merges++;
}
}
}
if (i0 != id) {
kv_self.cells[id] = cell0;
}
id++;
}
}
kv_self.head = id;
kv_self.used = id;
for (uint32_t i = id; i < kv_size; ++i) {
kv_self.cells[i] = llama_kv_cell();
}
LLAMA_LOG_INFO("(tmp log) KV compress pairs: %u\n", n_merges);
ggml_type_traits_t tt_k;
ggml_type_traits_t tt_v;
tt_k = ggml_internal_get_type_traits(kv_self.type_k);
tt_v = ggml_internal_get_type_traits(kv_self.type_v);
for (uint32_t il = 0; il < n_layer; ++il) {
// update keys
{
const int64_t ne = n_embd_k_gqa*kv_size;
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, ne);
buf_q.resize(k_size);
buf_src_f32.resize(ne);
buf_dst_f32.resize(ne);
ggml_backend_tensor_get(kv_self.k_l[il], buf_q.data(), 0, buf_q.size());
tt_k.to_float(buf_q.data(), buf_src_f32.data(), ne);
std::fill(buf_dst_f32.begin(), buf_dst_f32.end(), 0);
for (uint32_t i = 0; i < kv_size; ++i) {
if (!infos[i].merged) {
continue;
}
const uint32_t id = infos[i].id;
// merge using averaging
{
const float scale = 1.0f/float(infos[id].cnt);
const int64_t os = i*n_embd_k_gqa;
const int64_t od = id*n_embd_k_gqa;
for (uint32_t j = 0; j < n_embd_k_gqa; ++j) {
buf_dst_f32[od + j] += buf_src_f32[os + j]*scale;
}
}
}
tt_k.from_float(buf_dst_f32.data(), buf_q.data(), ne);
ggml_backend_tensor_set(kv_self.k_l[il], buf_q.data(), 0, buf_q.size());
}
// update values (note: they are transposed)
{
const int64_t ne = n_embd_v_gqa*kv_size;
const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, ne);
buf_q.resize(v_size);
buf_src_f32.resize(ne);
buf_dst_f32.resize(ne);
ggml_backend_tensor_get(kv_self.v_l[il], buf_q.data(), 0, buf_q.size());
tt_v.to_float(buf_q.data(), buf_src_f32.data(), ne);
std::fill(buf_dst_f32.begin(), buf_dst_f32.end(), 0);
for (uint32_t i = 0; i < kv_size; ++i) {
if (!infos[i].merged) {
continue;
}
const uint32_t id = infos[i].id;
// merge using averaging
{
const float scale = 1.0f/float(infos[id].cnt);
//printf("i: %d -> id: %d, scale: %f\n", i, id, scale);
const int64_t os = i;
const int64_t od = id;
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
buf_dst_f32[od + j*kv_size] += buf_src_f32[os + j*kv_size]*scale;
}
}
}
tt_v.from_float(buf_dst_f32.data(), buf_q.data(), ne);
ggml_backend_tensor_set(kv_self.v_l[il], buf_q.data(), 0, buf_q.size());
}
}
const int64_t t_end = ggml_time_us();
LLAMA_LOG_INFO("(tmp log) KV compress time: %.3f ms\n", (t_end - t_start)/1000.0);
kv_self.compress_delta = -1;
}
}
//
@ -12083,6 +12281,10 @@ llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id se
return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
}
void llama_kv_cache_compress(struct llama_context * ctx, llama_pos delta) {
llama_kv_cache_compress(ctx->kv_self, delta);
}
void llama_kv_cache_update(struct llama_context * ctx) {
llama_kv_cache_update_internal(*ctx);
}

5
llama.h
View file

@ -552,6 +552,11 @@ extern "C" {
struct llama_context * ctx,
llama_seq_id seq_id);
// [EXPERIMENTAL] Compress the data in the KV cache
LLAMA_API void llama_kv_cache_compress(
struct llama_context * ctx,
llama_pos delta);
// Apply the KV cache updates (such as K-shifts) to the KV data
LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);