kv-cache : simplify the interface (#13660)
* kv-cache : simplify the interface ggml-ci * context : revert llama_batch_allocr position change ggml-ci
This commit is contained in:
Georgi Gerganov
GitHub
parent
b44890df2e
commit
797f2ac062
9 changed files with 89 additions and 153 deletions
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@ -1,5 +1,6 @@
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#include "llama-batch.h"
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#include <cassert>
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#include <cstring>
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#include <algorithm>
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@ -281,9 +282,10 @@ llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0
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batch = in_batch;
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GGML_ASSERT(batch.n_tokens > 0);
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if (!batch.pos) {
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assert(p0 >= 0);
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pos.resize(batch.n_tokens);
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for (int32_t i = 0; i < batch.n_tokens; i++) {
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pos[i] = i + p0;
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pos[i] = p0 + i;
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}
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batch.pos = pos.data();
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}
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@ -857,11 +857,17 @@ int llama_context::decode(llama_batch & inp_batch) {
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return -1;
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}
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if (!inp_batch.pos) {
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if (inp_batch.seq_id) {
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LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__);
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return -1;
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}
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}
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llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
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// temporary allocate memory for the input batch if needed
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// TODO: this is incorrect for multiple sequences because get_pos_max() is the maximum across all sequences
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llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->get_pos_max() + 1);
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llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->seq_pos_max(0) + 1);
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const llama_batch & batch = batch_allocr.batch;
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@ -2292,22 +2298,47 @@ int32_t llama_apply_adapter_cvec(
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// kv cache
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//
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// deprecated
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int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
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const auto * kv = ctx->get_kv_self();
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if (!kv) {
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return 0;
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}
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return kv->get_n_tokens();
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int32_t res = 0;
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for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
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const llama_pos p0 = kv->seq_pos_min(s);
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const llama_pos p1 = kv->seq_pos_max(s);
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if (p0 >= 0) {
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res += (p1 - p0) + 1;
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}
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}
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return res;
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}
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// deprecated
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// note: this is the same as above - will be removed anyway, so it's ok
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int32_t llama_kv_self_used_cells(const llama_context * ctx) {
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const auto * kv = ctx->get_kv_self();
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if (!kv) {
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return 0;
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}
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return kv->get_used_cells();
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int32_t res = 0;
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for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
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const llama_pos p0 = kv->seq_pos_min(s);
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const llama_pos p1 = kv->seq_pos_max(s);
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if (p0 >= 0) {
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res += (p1 - p0) + 1;
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}
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}
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return res;
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}
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void llama_kv_self_clear(llama_context * ctx) {
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@ -30,13 +30,14 @@ llama_kv_cache_unified::llama_kv_cache_unified(
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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uint32_t padding,
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uint32_t n_seq_max,
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uint32_t n_pad,
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uint32_t n_swa,
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llama_swa_type swa_type) : model(model), hparams(model.hparams), v_trans(v_trans), padding(padding), n_swa(n_swa), swa_type(swa_type) {
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GGML_ASSERT(kv_size % padding == 0 && "kv_size must be a multiple of padding");
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llama_swa_type swa_type) :
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model(model), hparams(model.hparams), v_trans(v_trans),
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n_seq_max(n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
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this->type_k = type_k;
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this->type_v = type_v;
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GGML_ASSERT(kv_size % n_pad == 0);
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// create a context for each buffer type
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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@ -129,8 +130,8 @@ llama_kv_cache_unified::llama_kv_cache_unified(
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const size_t memory_size_k = size_k_bytes();
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const size_t memory_size_v = size_v_bytes();
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LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6d cells, %3d layers), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
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(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(),
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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__,
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(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max,
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ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
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ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
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}
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@ -442,7 +443,7 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
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void llama_kv_cache_unified::defrag_sched(float thold) {
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// - do not defrag small contexts (i.e. < 2048 tokens)
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// - count the padding towards the number of used tokens
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const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + padding)/n)) : 0.0f;
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const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + n_pad)/n)) : 0.0f;
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// queue defragmentation for next llama_kv_cache_update
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if (fragmentation > thold) {
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@ -558,7 +559,7 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
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// a heuristic, to avoid attending the full cache if it is not yet utilized
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// after enough generations, the benefit from this heuristic disappears
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// if we start defragmenting the cache, the benefit from this will be more important
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n = std::min(size, std::max(padding, GGML_PAD(cell_max(), padding)));
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n = std::min(size, std::max(n_pad, GGML_PAD(cell_max(), n_pad)));
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#ifdef FIND_SLOT_DEBUG
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LLAMA_LOG_WARN("end: n = %5d, used = %5d, head = %5d, n_swa = %5d\n", n, used, head, n_swa);
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@ -567,20 +568,6 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
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return true;
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}
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int32_t llama_kv_cache_unified::get_n_tokens() const {
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int32_t result = 0;
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for (uint32_t i = 0; i < size; i++) {
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result += cells[i].seq_id.size();
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}
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return result;
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}
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int32_t llama_kv_cache_unified::get_used_cells() const {
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return used;
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}
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bool llama_kv_cache_unified::get_can_shift() const {
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return true;
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}
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@ -802,16 +789,6 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
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}
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}
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llama_pos llama_kv_cache_unified::get_pos_max() const {
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llama_pos pos_max = -1;
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for (const auto & cell : cells) {
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pos_max = std::max(pos_max, cell.pos);
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}
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return pos_max;
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}
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size_t llama_kv_cache_unified::total_size() const {
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size_t size = 0;
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@ -1501,11 +1478,8 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
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llama_seq_id seq_id;
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io.read_to(&seq_id, sizeof(seq_id));
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// TODO: llama_kv_cache_unified should have a notion of max sequences
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//if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
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if (seq_id < 0) {
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//LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
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LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, inf)\n", __func__, seq_id);
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if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
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LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, n_seq_max);
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return false;
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}
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@ -1655,17 +1629,17 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
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ggml_type type_v,
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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bool swa_full,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_batch,
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uint32_t padding) : hparams(model.hparams) {
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uint32_t n_pad) : hparams(model.hparams) {
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llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
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llama_kv_cache_unified::layer_filter_cb filter_swa = [&](int32_t il) { return model.hparams.is_swa(il); };
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const uint32_t size_base = kv_size;
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uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, padding));
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uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, n_pad));
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// when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size and disable pruning
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if (swa_full) {
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@ -1680,14 +1654,14 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
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kv_base = std::make_unique<llama_kv_cache_unified>(
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model, std::move(filter_base), type_k, type_v,
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v_trans, offload, size_base, padding,
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v_trans, offload, size_base, n_seq_max, n_pad,
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0, LLAMA_SWA_TYPE_NONE);
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LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
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kv_swa = std::make_unique<llama_kv_cache_unified>(
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model, std::move(filter_swa), type_k, type_v,
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v_trans, offload, size_swa, padding,
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v_trans, offload, size_swa, n_seq_max, n_pad,
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hparams.n_swa, hparams.swa_type);
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}
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@ -1810,18 +1784,6 @@ bool llama_kv_cache_unified_iswa::find_slot(const llama_ubatch & batch) {
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return res;
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}
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int32_t llama_kv_cache_unified_iswa::get_n_tokens() const {
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return kv_base->get_n_tokens();
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}
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int32_t llama_kv_cache_unified_iswa::get_used_cells() const {
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return kv_base->get_used_cells();
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}
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llama_pos llama_kv_cache_unified_iswa::get_pos_max() const {
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return kv_base->get_pos_max();
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}
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bool llama_kv_cache_unified_iswa::get_can_shift() const {
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return kv_base->get_size() == kv_swa->get_size();
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}
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@ -1853,19 +1815,17 @@ llama_kv_cache_recurrent::llama_kv_cache_recurrent(
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ggml_type type_k,
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ggml_type type_v,
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bool offload,
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uint32_t kv_size) : hparams(model.hparams) {
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uint32_t kv_size,
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uint32_t n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
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const int32_t n_layer = hparams.n_layer;
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LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d\n",
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__func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
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LLAMA_LOG_INFO("%s: kv_size = %u, n_seq_max = %u, type_k = '%s', type_v = '%s', n_layer = %d\n",
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__func__, kv_size, n_seq_max, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
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head = 0;
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size = kv_size;
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used = 0;
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this->type_k = type_k;
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this->type_v = type_v;
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cells.clear();
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cells.resize(kv_size);
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@ -2203,8 +2163,8 @@ void llama_kv_cache_recurrent::commit() {
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pending.ranges.clear();
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}
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bool llama_kv_cache_recurrent::update(llama_context & lctx) {
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GGML_UNUSED(lctx);
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bool llama_kv_cache_recurrent::update(llama_context & ctx) {
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GGML_UNUSED(ctx);
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return false;
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}
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@ -2265,7 +2225,7 @@ bool llama_kv_cache_recurrent::find_slot(
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if (seq_id < 0 || (uint32_t) seq_id >= size) {
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// too big seq_id
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// TODO: would it be possible to resize the cache instead?
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LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size);
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LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%u Try using a bigger --parallel value\n", __func__, seq_id, n_seq_max);
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return false;
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}
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if (j > 0) {
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@ -2408,29 +2368,6 @@ bool llama_kv_cache_recurrent::find_slot(
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return n >= n_seqs;
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}
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int32_t llama_kv_cache_recurrent::get_n_tokens() const {
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int32_t result = 0;
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for (uint32_t i = 0; i < size; i++) {
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result += cells[i].seq_id.size();
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}
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return result;
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}
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int32_t llama_kv_cache_recurrent::get_used_cells() const {
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return used;
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}
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llama_pos llama_kv_cache_recurrent::get_pos_max() const {
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llama_pos pos_max = -1;
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for (const auto & cell : cells) {
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pos_max = std::max(pos_max, cell.pos);
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}
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return pos_max;
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}
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bool llama_kv_cache_recurrent::get_can_shift() const {
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return false;
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}
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@ -55,10 +55,7 @@ struct llama_kv_cache : public llama_memory_i {
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// =============================================================================================================
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// getters
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virtual int32_t get_n_tokens() const = 0;
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virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache
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virtual llama_pos get_pos_max() const = 0;
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virtual bool get_can_shift() const = 0;
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virtual bool get_can_shift() const = 0;
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bool get_can_edit() const override { return get_can_shift(); }
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@ -108,7 +105,8 @@ public:
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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uint32_t padding,
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uint32_t n_seq_max,
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uint32_t n_pad,
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uint32_t n_swa,
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llama_swa_type swa_type);
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@ -150,12 +148,6 @@ public:
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// to the first cell of the slot.
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bool find_slot(const llama_ubatch & batch) override;
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int32_t get_n_tokens() const override;
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int32_t get_used_cells() const override;
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// TODO: better data structures to reduce the cost of this operation
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llama_pos get_pos_max() const override;
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bool get_can_shift() const override;
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// state write/load
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@ -228,16 +220,15 @@ private:
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// computed before each graph build
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uint32_t n = 0;
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// required padding
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uint32_t padding = 1;
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const uint32_t n_seq_max = 1;
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ggml_type type_k = GGML_TYPE_F16;
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ggml_type type_v = GGML_TYPE_F16;
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// required padding
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const uint32_t n_pad = 1;
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// SWA
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uint32_t n_swa = 0;
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const uint32_t n_swa = 0;
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llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
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const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
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std::vector<ggml_context_ptr> ctxs;
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std::vector<ggml_backend_buffer_ptr> bufs;
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@ -317,11 +308,11 @@ public:
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ggml_type type_v,
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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bool swa_full,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_batch,
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uint32_t padding);
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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;
|
||||
|
|
|
|||
|
|
@ -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);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue