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llama : add cell_max heuristic for more efficient kv_cache

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This commit is contained in:
Georgi Gerganov 2023-09-18 15:31:24 +03:00
parent 9f42e75489
commit 6952a460b9
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GPG Key ID: 449E073F9DC10735
2 changed files with 102 additions and 29 deletions
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119
llama.cpp
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@ -1023,6 +1023,9 @@ struct llama_kv_cache {
uint32_t head = 0;
uint32_t size = 0;
// largest index of an occupied cell (used for a basic optimization heuristic)
uint32_t cell_max = 0;
std::vector<llama_kv_cell> cells;
struct ggml_tensor * k = NULL;
@ -1226,6 +1229,8 @@ static bool llama_kv_cache_init(
cache.head = 0;
cache.size = n_ctx;
cache.cell_max = 0;
cache.cells.clear();
cache.cells.resize(n_ctx);
@ -1311,6 +1316,16 @@ static bool llama_kv_cache_find_slot(
return true;
}
void llama_kv_cache_update_cell_max(struct llama_kv_cache & cache) {
cache.cell_max = 0;
for (uint32_t i = 0; i < cache.size; i++) {
if (cache.cells[i].pos >= 0) {
cache.cell_max = i + 1;
}
}
}
void llama_kv_cache_clear(struct llama_kv_cache & cache, int32_t p0, int32_t p1) {
cache.head = p0;
@ -1321,6 +1336,8 @@ void llama_kv_cache_clear(struct llama_kv_cache & cache, int32_t p0, int32_t p1)
cache.cells[i].pos = -1;
cache.cells[i].seq_id.clear();
}
llama_kv_cache_update_cell_max(cache);
}
//
@ -2547,6 +2564,7 @@ static struct ggml_cgraph * llm_build_llama(
const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = kv_self.cell_max + n_tokens;
auto & buf_compute = lctx.buf_compute;
@ -2621,7 +2639,7 @@ static struct ggml_cgraph * llm_build_llama(
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_ctx, n_tokens, 1);
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
@ -2629,9 +2647,19 @@ static struct ggml_cgraph * llm_build_llama(
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_ctx; ++i) {
if (!kv_self.cells[i].has_seq_id(batch.seq_id[j]) || kv_self.cells[i].pos > batch.pos[j]) {
data[h*(n_ctx*n_tokens) + j*n_ctx + i] = -INFINITY;
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
// TODO: temporary heuristic verification - if this fails then there is a bug with cell_max computation
for (int i = n_kv; i < n_ctx; ++i) {
if (kv_self.cells[i].has_seq_id(seq_id) && kv_self.cells[i].pos >= 0) {
GGML_ASSERT(false && "cell_max is too small - this might indicate a bug");
}
}
}
@ -2725,7 +2753,7 @@ static struct ggml_cgraph * llm_build_llama(
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k,
n_embd_head, n_ctx, n_head_kv,
n_embd_head, n_kv, n_head_kv,
ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
@ -2738,7 +2766,7 @@ static struct ggml_cgraph * llm_build_llama(
ggml_set_name(KQ, "KQ");
// KQ_scaled = KQ / sqrt(n_embd_head)
// KQ_scaled shape [n_ctx, n_tokens, n_head, 1]
// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
offload_func_kq(KQ_scaled);
ggml_set_name(KQ_scaled, "KQ_scaled");
@ -2756,7 +2784,7 @@ static struct ggml_cgraph * llm_build_llama(
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_ctx, n_embd_head, n_head_kv,
n_kv, n_embd_head, n_head_kv,
ggml_element_size(kv_self.v)*n_ctx,
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
@ -2901,6 +2929,7 @@ static struct ggml_cgraph * llm_build_baichaun(
const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = kv_self.cell_max + n_tokens;
auto & buf_compute = lctx.buf_compute;
@ -2975,7 +3004,7 @@ static struct ggml_cgraph * llm_build_baichaun(
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_ctx, n_tokens, 1);
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
@ -2983,9 +3012,19 @@ static struct ggml_cgraph * llm_build_baichaun(
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_ctx; ++i) {
if (!kv_self.cells[i].has_seq_id(batch.seq_id[j]) || kv_self.cells[i].pos > batch.pos[j]) {
data[h*(n_ctx*n_tokens) + j*n_ctx + i] = -INFINITY;
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
// TODO: temporary heuristic verification - if this fails then there is a bug with cell_max computation
for (int i = n_kv; i < n_ctx; ++i) {
if (kv_self.cells[i].has_seq_id(seq_id) && kv_self.cells[i].pos >= 0) {
GGML_ASSERT(false && "cell_max is too small - this might indicate a bug");
}
}
}
@ -3092,7 +3131,7 @@ static struct ggml_cgraph * llm_build_baichaun(
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k,
n_embd_head, n_ctx, n_head_kv,
n_embd_head, n_kv, n_head_kv,
ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
@ -3135,7 +3174,7 @@ static struct ggml_cgraph * llm_build_baichaun(
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_ctx, n_embd_head, n_head_kv,
n_kv, n_embd_head, n_head_kv,
ggml_element_size(kv_self.v)*n_ctx,
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
@ -3272,6 +3311,7 @@ static struct ggml_cgraph * llm_build_falcon(
const int n_gpu_layers = model.n_gpu_layers;
const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = kv_self.cell_max + n_tokens;
auto & buf_compute = lctx.buf_compute;
@ -3346,7 +3386,7 @@ static struct ggml_cgraph * llm_build_falcon(
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_ctx, n_tokens, 1);
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
@ -3354,9 +3394,19 @@ static struct ggml_cgraph * llm_build_falcon(
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_ctx; ++i) {
if (!kv_self.cells[i].has_seq_id(batch.seq_id[j]) || kv_self.cells[i].pos > batch.pos[j]) {
data[h*(n_ctx*n_tokens) + j*n_ctx + i] = -INFINITY;
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
// TODO: temporary heuristic verification - if this fails then there is a bug with cell_max computation
for (int i = n_kv; i < n_ctx; ++i) {
if (kv_self.cells[i].has_seq_id(seq_id) && kv_self.cells[i].pos >= 0) {
GGML_ASSERT(false && "cell_max is too small - this might indicate a bug");
}
}
}
@ -3479,7 +3529,7 @@ static struct ggml_cgraph * llm_build_falcon(
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k,
n_embd_head, n_ctx, n_head_kv,
n_embd_head, n_kv, n_head_kv,
ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
@ -3504,7 +3554,7 @@ static struct ggml_cgraph * llm_build_falcon(
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_ctx, n_embd_head, n_head_kv,
n_kv, n_embd_head, n_head_kv,
ggml_element_size(kv_self.v)*n_ctx,
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
@ -3598,6 +3648,7 @@ static struct ggml_cgraph * llm_build_starcoder(
const float norm_eps = hparams.f_norm_eps;
const int32_t n_tokens = batch.n_tokens;
const int32_t n_kv = kv_self.cell_max + n_tokens;
auto & buf_compute = lctx.buf_compute;
@ -3664,7 +3715,7 @@ static struct ggml_cgraph * llm_build_starcoder(
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_ctx, n_tokens, 1);
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
ggml_allocr_alloc(lctx.alloc, KQ_mask);
if (!ggml_allocr_is_measure(lctx.alloc)) {
float * data = (float *) KQ_mask->data;
@ -3672,9 +3723,19 @@ static struct ggml_cgraph * llm_build_starcoder(
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_ctx; ++i) {
if (!kv_self.cells[i].has_seq_id(batch.seq_id[j]) || kv_self.cells[i].pos > batch.pos[j]) {
data[h*(n_ctx*n_tokens) + j*n_ctx + i] = -INFINITY;
const llama_pos pos = batch.pos[j];
const llama_seq_id seq_id = batch.seq_id[j];
for (int i = 0; i < n_kv; ++i) {
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
}
}
// TODO: temporary heuristic verification - if this fails then there is a bug with cell_max computation
for (int i = n_kv; i < n_ctx; ++i) {
if (kv_self.cells[i].has_seq_id(seq_id) && kv_self.cells[i].pos >= 0) {
GGML_ASSERT(false && "cell_max is too small - this might indicate a bug");
}
}
}
@ -3727,7 +3788,7 @@ static struct ggml_cgraph * llm_build_starcoder(
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k,
n_embd_head, n_ctx, n_head_kv,
n_embd_head, n_kv, n_head_kv,
ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
@ -3753,7 +3814,7 @@ static struct ggml_cgraph * llm_build_starcoder(
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_ctx, n_embd_head, n_head_kv,
n_kv, n_embd_head, n_head_kv,
ggml_element_size(kv_self.v)*n_ctx,
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
@ -3974,8 +4035,9 @@ static bool llama_eval_internal(
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
#endif
// update the kv ring buffer head
lctx.kv_self.head += n_tokens;
// update the kv ring buffer
lctx.kv_self.head += n_tokens;
lctx.kv_self.cell_max = std::max(lctx.kv_self.cell_max, lctx.kv_self.head);
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
@ -7040,6 +7102,9 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
}
ctx->kv_self.head = kv_ntok;
ctx->kv_self.size = kv_size;
ctx->kv_self.cell_max = kv_ntok;
}
const size_t nread = inp - src;

12
llama.h
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@ -316,15 +316,19 @@ extern "C" {
int n_threads);
//
// KV cache API
// KV cache
//
// Returns the number of tokens in the KV cache
LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
"avoid using this, it will be removed in the future");
"avoid using this, it will be removed in the future, instead - count the tokens in user code");
LLAMA_API void llama_kv_clear(struct llama_context * ctx, int32_t p0, int32_t p1);
//
// State / sessions
//
// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens
LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
@ -342,6 +346,10 @@ extern "C" {
LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
//
// Decoding
//
// Run the llama inference to obtain the logits and probabilities for the next token.
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls