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kv-cache : simplify the interface (#13660)

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* kv-cache : simplify the interface

ggml-ci

* context : revert llama_batch_allocr position change

ggml-ci
This commit is contained in:
Georgi Gerganov
2025-05-21 15:11:13 +03:00
committed by GitHub
parent b44890df2e
commit 797f2ac062
9 changed files with 89 additions and 153 deletions
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4
examples/simple-chat/simple-chat.cpp
View File

@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
auto generate = [&](const std::string & prompt) {
std::string response;
const bool is_first = llama_kv_self_used_cells(ctx) == 0;
const bool is_first = llama_kv_self_seq_pos_max(ctx, 0) == 0;
// tokenize the prompt
const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
@ -113,7 +113,7 @@ int main(int argc, char ** argv) {
while (true) {
// check if we have enough space in the context to evaluate this batch
int n_ctx = llama_n_ctx(ctx);
int n_ctx_used = llama_kv_self_used_cells(ctx);
int n_ctx_used = llama_kv_self_seq_pos_max(ctx, 0);
if (n_ctx_used + batch.n_tokens > n_ctx) {
printf("\033[0m\n");
fprintf(stderr, "context size exceeded\n");

6
include/llama.h
View File

@ -610,10 +610,12 @@ extern "C" {
// Returns the number of tokens in the KV cache (slow, use only for debug)
// If a KV cell has multiple sequences assigned to it, it will be counted multiple times
LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx);
DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx),
"Use llama_kv_self_seq_pos_max() instead");
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx);
DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx),
"Use llama_kv_self_seq_pos_max() instead");
// Clear the KV cache - both cell info is erased and KV data is zeroed
LLAMA_API void llama_kv_self_clear(

4
src/llama-batch.cpp
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@ -1,5 +1,6 @@
#include "llama-batch.h"
#include <cassert>
#include <cstring>
#include <algorithm>
@ -281,9 +282,10 @@ llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0
batch = in_batch;
GGML_ASSERT(batch.n_tokens > 0);
if (!batch.pos) {
assert(p0 >= 0);
pos.resize(batch.n_tokens);
for (int32_t i = 0; i < batch.n_tokens; i++) {
pos[i] = i + p0;
pos[i] = p0 + i;
}
batch.pos = pos.data();
}

39
src/llama-context.cpp
View File

@ -857,11 +857,17 @@ int llama_context::decode(llama_batch & inp_batch) {
return -1;
}
if (!inp_batch.pos) {
if (inp_batch.seq_id) {
LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__);
return -1;
}
}
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
// temporary allocate memory for the input batch if needed
// TODO: this is incorrect for multiple sequences because get_pos_max() is the maximum across all sequences
llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->get_pos_max() + 1);
llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->seq_pos_max(0) + 1);
const llama_batch & batch = batch_allocr.batch;
@ -2292,22 +2298,47 @@ int32_t llama_apply_adapter_cvec(
// kv cache
//
// deprecated
int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return 0;
}
return kv->get_n_tokens();
int32_t res = 0;
for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
const llama_pos p0 = kv->seq_pos_min(s);
const llama_pos p1 = kv->seq_pos_max(s);
if (p0 >= 0) {
res += (p1 - p0) + 1;
}
}
return res;
}
// deprecated
// note: this is the same as above - will be removed anyway, so it's ok
int32_t llama_kv_self_used_cells(const llama_context * ctx) {
const auto * kv = ctx->get_kv_self();
if (!kv) {
return 0;
}
return kv->get_used_cells();
int32_t res = 0;
for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
const llama_pos p0 = kv->seq_pos_min(s);
const llama_pos p1 = kv->seq_pos_max(s);
if (p0 >= 0) {
res += (p1 - p0) + 1;
}
}
return res;
}
void llama_kv_self_clear(llama_context * ctx) {

111
src/llama-kv-cache.cpp
View File

@ -30,13 +30,14 @@ llama_kv_cache_unified::llama_kv_cache_unified(
bool v_trans,
bool offload,
uint32_t kv_size,
uint32_t padding,
uint32_t n_seq_max,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type) : model(model), hparams(model.hparams), v_trans(v_trans), padding(padding), n_swa(n_swa), swa_type(swa_type) {
GGML_ASSERT(kv_size % padding == 0 && "kv_size must be a multiple of padding");
llama_swa_type swa_type) :
model(model), hparams(model.hparams), v_trans(v_trans),
n_seq_max(n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
this->type_k = type_k;
this->type_v = type_v;
GGML_ASSERT(kv_size % n_pad == 0);
// create a context for each buffer type
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
@ -129,8 +130,8 @@ llama_kv_cache_unified::llama_kv_cache_unified(
const size_t memory_size_k = size_k_bytes();
const size_t memory_size_v = size_v_bytes();
LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6d cells, %3d layers), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(),
LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max,
ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
}
@ -442,7 +443,7 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
void llama_kv_cache_unified::defrag_sched(float thold) {
// - do not defrag small contexts (i.e. < 2048 tokens)
// - count the padding towards the number of used tokens
const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + padding)/n)) : 0.0f;
const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + n_pad)/n)) : 0.0f;
// queue defragmentation for next llama_kv_cache_update
if (fragmentation > thold) {
@ -558,7 +559,7 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
// a heuristic, to avoid attending the full cache if it is not yet utilized
// after enough generations, the benefit from this heuristic disappears
// if we start defragmenting the cache, the benefit from this will be more important
n = std::min(size, std::max(padding, GGML_PAD(cell_max(), padding)));
n = std::min(size, std::max(n_pad, GGML_PAD(cell_max(), n_pad)));
#ifdef FIND_SLOT_DEBUG
LLAMA_LOG_WARN("end: n = %5d, used = %5d, head = %5d, n_swa = %5d\n", n, used, head, n_swa);
@ -567,20 +568,6 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
return true;
}
int32_t llama_kv_cache_unified::get_n_tokens() const {
int32_t result = 0;
for (uint32_t i = 0; i < size; i++) {
result += cells[i].seq_id.size();
}
return result;
}
int32_t llama_kv_cache_unified::get_used_cells() const {
return used;
}
bool llama_kv_cache_unified::get_can_shift() const {
return true;
}
@ -802,16 +789,6 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
}
}
llama_pos llama_kv_cache_unified::get_pos_max() const {
llama_pos pos_max = -1;
for (const auto & cell : cells) {
pos_max = std::max(pos_max, cell.pos);
}
return pos_max;
}
size_t llama_kv_cache_unified::total_size() const {
size_t size = 0;
@ -1501,11 +1478,8 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
llama_seq_id seq_id;
io.read_to(&seq_id, sizeof(seq_id));
// TODO: llama_kv_cache_unified should have a notion of max sequences
//if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
if (seq_id < 0) {
//LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, inf)\n", __func__, seq_id);
if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, n_seq_max);
return false;
}
@ -1655,17 +1629,17 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
ggml_type type_v,
bool v_trans,
bool offload,
uint32_t kv_size,
bool swa_full,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_batch,
uint32_t padding) : hparams(model.hparams) {
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_batch, padding));
uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, n_pad));
// when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size and disable pruning
if (swa_full) {
@ -1680,14 +1654,14 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
kv_base = std::make_unique<llama_kv_cache_unified>(
model, std::move(filter_base), type_k, type_v,
v_trans, offload, size_base, padding,
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, padding,
v_trans, offload, size_swa, n_seq_max, n_pad,
hparams.n_swa, hparams.swa_type);
}
@ -1810,18 +1784,6 @@ bool llama_kv_cache_unified_iswa::find_slot(const llama_ubatch & batch) {
return res;
}
int32_t llama_kv_cache_unified_iswa::get_n_tokens() const {
return kv_base->get_n_tokens();
}
int32_t llama_kv_cache_unified_iswa::get_used_cells() const {
return kv_base->get_used_cells();
}
llama_pos llama_kv_cache_unified_iswa::get_pos_max() const {
return kv_base->get_pos_max();
}
bool llama_kv_cache_unified_iswa::get_can_shift() const {
return kv_base->get_size() == kv_swa->get_size();
}
@ -1853,19 +1815,17 @@ llama_kv_cache_recurrent::llama_kv_cache_recurrent(
ggml_type type_k,
ggml_type type_v,
bool offload,
uint32_t kv_size) : hparams(model.hparams) {
uint32_t kv_size,
uint32_t n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
const int32_t n_layer = hparams.n_layer;
LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d\n",
__func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
LLAMA_LOG_INFO("%s: kv_size = %u, n_seq_max = %u, type_k = '%s', type_v = '%s', n_layer = %d\n",
__func__, kv_size, n_seq_max, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
head = 0;
size = kv_size;
used = 0;
this->type_k = type_k;
this->type_v = type_v;
cells.clear();
cells.resize(kv_size);
@ -2203,8 +2163,8 @@ void llama_kv_cache_recurrent::commit() {
pending.ranges.clear();
}
bool llama_kv_cache_recurrent::update(llama_context & lctx) {
GGML_UNUSED(lctx);
bool llama_kv_cache_recurrent::update(llama_context & ctx) {
GGML_UNUSED(ctx);
return false;
}
@ -2265,7 +2225,7 @@ bool llama_kv_cache_recurrent::find_slot(
if (seq_id < 0 || (uint32_t) seq_id >= size) {
// too big seq_id
// TODO: would it be possible to resize the cache instead?
LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size);
LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%u Try using a bigger --parallel value\n", __func__, seq_id, n_seq_max);
return false;
}
if (j > 0) {
@ -2408,29 +2368,6 @@ bool llama_kv_cache_recurrent::find_slot(
return n >= n_seqs;
}
int32_t llama_kv_cache_recurrent::get_n_tokens() const {
int32_t result = 0;
for (uint32_t i = 0; i < size; i++) {
result += cells[i].seq_id.size();
}
return result;
}
int32_t llama_kv_cache_recurrent::get_used_cells() const {
return used;
}
llama_pos llama_kv_cache_recurrent::get_pos_max() const {
llama_pos pos_max = -1;
for (const auto & cell : cells) {
pos_max = std::max(pos_max, cell.pos);
}
return pos_max;
}
bool llama_kv_cache_recurrent::get_can_shift() const {
return false;
}

51
src/llama-kv-cache.h
View File

@ -55,10 +55,7 @@ struct llama_kv_cache : public llama_memory_i {
// =============================================================================================================
// getters
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 llama_pos get_pos_max() const = 0;
virtual bool get_can_shift() const = 0;
virtual bool get_can_shift() const = 0;
bool get_can_edit() const override { return get_can_shift(); }
@ -108,7 +105,8 @@ public:
bool v_trans,
bool offload,
uint32_t kv_size,
uint32_t padding,
uint32_t n_seq_max,
uint32_t n_pad,
uint32_t n_swa,
llama_swa_type swa_type);
@ -150,12 +148,6 @@ public:
// to the first cell of the slot.
bool find_slot(const llama_ubatch & batch) override;
int32_t get_n_tokens() const override;
int32_t get_used_cells() const override;
// TODO: better data structures to reduce the cost of this operation
llama_pos get_pos_max() const override;
bool get_can_shift() const override;
// state write/load
@ -228,16 +220,15 @@ private:
// computed before each graph build
uint32_t n = 0;
// required padding
uint32_t padding = 1;
const uint32_t n_seq_max = 1;
ggml_type type_k = GGML_TYPE_F16;
ggml_type type_v = GGML_TYPE_F16;
// required padding
const uint32_t n_pad = 1;
// SWA
uint32_t n_swa = 0;
const uint32_t n_swa = 0;
llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
@ -317,11 +308,11 @@ public:
ggml_type type_v,
bool v_trans,
bool offload,
uint32_t kv_size,
bool swa_full,
uint32_t kv_size,
uint32_t n_seq_max,
uint32_t n_batch,
uint32_t padding);
uint32_t n_pad);
~llama_kv_cache_unified_iswa() = default;
@ -358,12 +349,6 @@ public:
bool find_slot(const llama_ubatch & batch) override;
int32_t get_n_tokens() const override;
int32_t get_used_cells() const override;
// TODO: better data structures to reduce the cost of this operation
llama_pos get_pos_max() const override;
bool get_can_shift() const override;
// state write/load
@ -432,7 +417,8 @@ public:
ggml_type type_k,
ggml_type type_v,
bool offload,
uint32_t kv_size);
uint32_t kv_size,
uint32_t n_seq_max);
~llama_kv_cache_recurrent() = default;
@ -444,7 +430,7 @@ public:
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_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;
@ -458,7 +444,7 @@ public:
void restore() override;
void commit() override;
bool update(llama_context & lctx) override;
bool update(llama_context & ctx) override;
void defrag_sched(float thold) override;
@ -469,12 +455,6 @@ public:
bool find_slot(const llama_ubatch & batch) override;
int32_t get_n_tokens() const override;
int32_t get_used_cells() const override;
// TODO: better data structures to reduce the cost of this operation
llama_pos get_pos_max() const override;
bool get_can_shift() const override;
// TODO: temporary methods - they are not really const as they do const_cast<>, fix this
@ -514,8 +494,7 @@ private:
std::vector<slot_range> ranges;
} pending;
ggml_type type_k = GGML_TYPE_F16;
ggml_type type_v = GGML_TYPE_F16;
const uint32_t n_seq_max = 1;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;

6
src/llama-model.cpp
View File

@ -13203,7 +13203,8 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
GGML_TYPE_F32,
GGML_TYPE_F32,
cparams.offload_kqv,
std::max((uint32_t) 1, cparams.n_seq_max));
std::max((uint32_t) 1, cparams.n_seq_max),
cparams.n_seq_max);
} break;
default:
{
@ -13222,8 +13223,8 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
params.type_v,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.n_ctx,
params.swa_full,
cparams.n_ctx,
cparams.n_seq_max,
cparams.n_batch,
padding);
@ -13238,6 +13239,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
!cparams.flash_attn,
cparams.offload_kqv,
cparams.n_ctx,
cparams.n_seq_max,
padding,
hparams.n_swa,
hparams.swa_type);

4
tools/run/run.cpp
View File

@ -936,7 +936,7 @@ static int apply_chat_template(const struct common_chat_templates * tmpls, Llama
// Function to tokenize the prompt
static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
std::vector<llama_token> & prompt_tokens, const LlamaData & llama_data) {
const bool is_first = llama_kv_self_used_cells(llama_data.context.get()) == 0;
const bool is_first = llama_kv_self_seq_pos_max(llama_data.context.get(), 0) == 0;
const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
prompt_tokens.resize(n_prompt_tokens);
@ -952,7 +952,7 @@ static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt
// Check if we have enough space in the context to evaluate this batch
static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) {
const int n_ctx = llama_n_ctx(ctx.get());
const int n_ctx_used = llama_kv_self_used_cells(ctx.get());
const int n_ctx_used = llama_kv_self_seq_pos_max(ctx.get(), 0);
if (n_ctx_used + batch.n_tokens > n_ctx) {
printf(LOG_COL_DEFAULT "\n");
printe("context size exceeded\n");

17
tools/server/server.cpp
View File

@ -1137,9 +1137,6 @@ struct server_task_result_metrics : server_task_result {
int n_tasks_deferred;
int64_t t_start;
int32_t kv_cache_tokens_count;
int32_t kv_cache_used_cells;
// TODO: somehow reuse server_metrics in the future, instead of duplicating the fields
uint64_t n_prompt_tokens_processed_total = 0;
uint64_t t_prompt_processing_total = 0;
@ -1179,9 +1176,6 @@ struct server_task_result_metrics : server_task_result {
{ "n_decode_total", n_decode_total },
{ "n_busy_slots_total", n_busy_slots_total },
{ "kv_cache_tokens_count", kv_cache_tokens_count },
{ "kv_cache_used_cells", kv_cache_used_cells },
{ "slots", slots_data },
};
}
@ -2771,9 +2765,6 @@ struct server_context {
res->n_tasks_deferred = queue_tasks.queue_tasks_deferred.size();
res->t_start = metrics.t_start;
res->kv_cache_tokens_count = llama_kv_self_n_tokens(ctx);
res->kv_cache_used_cells = llama_kv_self_used_cells(ctx);
res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total;
res->t_prompt_processing_total = metrics.t_prompt_processing_total;
res->n_tokens_predicted_total = metrics.n_tokens_predicted_total;
@ -3883,14 +3874,6 @@ int main(int argc, char ** argv) {
{"name", "predicted_tokens_seconds"},
{"help", "Average generation throughput in tokens/s."},
{"value", res_metrics->n_tokens_predicted ? 1.e3 / res_metrics->t_tokens_generation * res_metrics->n_tokens_predicted : 0.}
},{
{"name", "kv_cache_usage_ratio"},
{"help", "KV-cache usage. 1 means 100 percent usage."},
{"value", 1. * res_metrics->kv_cache_used_cells / params.n_ctx}
},{
{"name", "kv_cache_tokens"},
{"help", "KV-cache tokens."},
{"value", (uint64_t) res_metrics->kv_cache_tokens_count}
},{
{"name", "requests_processing"},
{"help", "Number of requests processing."},