llama : fix defrag bugs + enable by default

ggml-ci
This commit is contained in:
Georgi Gerganov 2024-02-26 17:25:08 +02:00
parent 8a533f0d90
commit 30c29f44cc
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GPG key ID: 449E073F9DC10735
2 changed files with 71 additions and 29 deletions

View file

@ -182,7 +182,7 @@ int main(int argc, char ** argv) {
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
llama_kv_cache_defrag (ctx); //llama_kv_cache_defrag (ctx);
llama_kv_cache_update (ctx); llama_kv_cache_update (ctx);
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
@ -213,7 +213,7 @@ int main(int argc, char ** argv) {
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
llama_kv_cache_defrag (ctx); //llama_kv_cache_defrag (ctx);
llama_kv_cache_update (ctx); llama_kv_cache_update (ctx);
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;

View file

@ -5114,16 +5114,16 @@ struct llm_build_context {
struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) { struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
for (int i = 0; i < n_kv; ++i) { for (uint32_t i = 0; i < ids.size(); ++i) {
const int id = ids[i]; const uint32_t id = ids[i];
if (i == id || id == n_kv) { if (i == id || id == ids.size()) {
continue; continue;
} }
int nm = 1; uint32_t nm = 1;
while (i + nm < n_kv && (int) ids[i + nm] == id + nm) { while (i + nm < ids.size() && ids[i + nm] == id + nm) {
nm++; nm++;
} }
@ -5155,6 +5155,8 @@ struct llm_build_context {
i += nm - 1; i += nm - 1;
} }
//LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
return gf; return gf;
} }
@ -7935,6 +7937,8 @@ static int llama_decode_internal(
batch.seq_id = seq_id_arr.data(); batch.seq_id = seq_id_arr.data();
} }
llama_kv_cache_update(&lctx);
// if we have enough unused cells before the current head -> // if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it // better to start searching from the beginning of the cache, hoping to fill it
if (kv_self.head > kv_self.used + 2*n_tokens) { if (kv_self.head > kv_self.used + 2*n_tokens) {
@ -7953,8 +7957,6 @@ static int llama_decode_internal(
//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
llama_kv_cache_update(&lctx);
ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_reset(lctx.sched);
ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
@ -8004,6 +8006,19 @@ static int llama_decode_internal(
} }
} }
// decide if we need to defrag the kv cache
// TODO: should become configurable
{
const float fragmentation = kv_self.n >= 512 ? 1.0f - float(kv_self.used + n_tokens)/float(kv_self.n) : 0.0f;
// queue defragmentation for next llama_kv_cache_update
if (fragmentation > 0.1f) {
LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
llama_kv_cache_defrag(kv_self);
}
}
#ifdef GGML_PERF #ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes) // print timing information per ggml operation (for debugging purposes)
// requires GGML_PERF to be defined // requires GGML_PERF to be defined
@ -8095,12 +8110,16 @@ static int llama_decode_internal(
static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
auto & kv_self = lctx.kv_self; auto & kv_self = lctx.kv_self;
const auto & hparams = lctx.model.hparams;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_kv = llama_kv_cache_cell_max(kv_self); const uint32_t n_kv = llama_kv_cache_cell_max(kv_self);
const uint32_t n_used = kv_self.used; const uint32_t n_used = kv_self.used;
assert(n_used <= n_kv); assert(n_used <= n_kv);
const int64_t t_start = ggml_time_us(); //const int64_t t_start = ggml_time_us();
// number of cells moved // number of cells moved
uint32_t n_moves = 0; uint32_t n_moves = 0;
@ -8124,15 +8143,29 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
// found a hole - fill it with data from the end of the cache // found a hole - fill it with data from the end of the cache
// determine the size of the hole
uint32_t nh = 1; uint32_t nh = 1;
// determine the size of the hole
while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) { while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
nh++; nh++;
} }
// starting from the end, find nh non-empty cells // in the worst case each move requires 6*n_layer tensors
//
// TODO: ideally this should be:
//
// if (6*(n_moves + nh)*n_layer > LLAMA_MAX_NODES) {
//
// but when I do that, the defrag graph can not fit due to not enough memory - not sure why
//
if (6*(n_moves + nh)*n_layer > LLAMA_MAX_NODES/2) {
break;
}
uint32_t nf = 0; uint32_t nf = 0;
uint32_t is = n_kv - 1; uint32_t is = n_kv - 1;
// starting from the end, find nh non-empty cells
for (; is > i0; --is) { for (; is > i0; --is) {
const auto & cell1 = kv_self.cells[is]; const auto & cell1 = kv_self.cells[is];
@ -8153,11 +8186,17 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
nf = 0; nf = 0;
uint32_t i1 = is;
// are we moving a continuous block of memory?
bool cont = false;
// go back and move the nf cells to the hole // go back and move the nf cells to the hole
for (uint32_t i1 = is; i1 < n_kv; ++i1) { for (; i1 < n_kv; ++i1) {
const auto & cell1 = kv_self.cells[i1]; auto & cell1 = kv_self.cells[i1];
if (cell1.is_empty() || ids[i1] != n_kv) { if (cell1.is_empty() || ids[i1] != n_kv) {
cont = false;
continue; continue;
} }
@ -8167,11 +8206,23 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
// move the cell meta data // move the cell meta data
kv_self.cells[i0 + nf] = cell1; kv_self.cells[i0 + nf] = cell1;
n_moves++; // clear the old cell and move the head there
cell1 = llama_kv_cell();
kv_self.head = n_used;
if (!cont) {
n_moves++;
cont = true;
}
nf++; nf++;
if (nf == nh) {
break;
}
} }
LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, n_kv, i0, i0 + nh); //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
i0 += nh - 1; i0 += nh - 1;
} }
@ -8180,15 +8231,9 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
return; return;
} }
LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves); //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
kv_self.head = n_used; //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
kv_self.used = n_used;
// zero the rest of the cells
for (uint32_t i = n_used; i < n_kv; ++i) {
kv_self.cells[i] = llama_kv_cell();
}
#if 0 #if 0
// CPU defrag // CPU defrag
@ -8200,9 +8245,6 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
// likely not worth the effort, as we have ggml_graph based defrag // likely not worth the effort, as we have ggml_graph based defrag
// //
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_k_gqa = hparams.n_embd_k_gqa();
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
@ -8271,9 +8313,9 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
llama_graph_compute(lctx, gf, lctx.cparams.n_threads); llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
#endif #endif
const int64_t t_end = ggml_time_us(); //const int64_t t_end = ggml_time_us();
LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0); //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
} }
static void llama_kv_cache_update_internal(struct llama_context & lctx) { static void llama_kv_cache_update_internal(struct llama_context & lctx) {