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kv-cache : use ggml_set_rows

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ggml-ci
This commit is contained in:
Georgi Gerganov
2025-06-19 19:26:47 +03:00
parent 8d94219a4a
commit 253304a8d5
9 changed files with 337 additions and 124 deletions
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39
src/llama-graph.cpp
View File

@ -281,12 +281,24 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
}
void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
if (self_kv_idxs) {
mctx->set_input_kv_idxs(self_kv_idxs, ubatch);
}
if (self_kq_mask) {
mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
if (self_kv_idxs) {
mctx->get_base()->set_input_kv_idxs(self_kv_idxs, ubatch);
}
if (self_kv_idxs_swa) {
mctx->get_swa()->set_input_kv_idxs(self_kv_idxs_swa, ubatch);
}
if (self_kq_mask) {
mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
@ -1198,6 +1210,9 @@ llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified()
const auto n_kv = mctx_cur->get_n_kv();
inp->self_kv_idxs = ggml_new_tensor_1d(ctx0, GGML_TYPE_I64, n_tokens);
ggml_set_input(inp->self_kv_idxs);
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
ggml_set_input(inp->self_kq_mask);
@ -1230,8 +1245,10 @@ ggml_tensor * llm_graph_context::build_attn(
// store to KV cache
{
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
const auto & kv_idxs = inp->get_kv_idxs();
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, kv_idxs, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, kv_idxs, il));
}
const auto & kq_mask = inp->get_kq_mask();
@ -1290,11 +1307,15 @@ ggml_tensor * llm_graph_context::build_attn(
// optionally store to KV cache
if (k_cur) {
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
const auto & kv_idxs = is_swa ? inp->get_kv_idxs_swa() : inp->get_kv_idxs();
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, kv_idxs, il));
}
if (v_cur) {
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
const auto & kv_idxs = is_swa ? inp->get_kv_idxs_swa() : inp->get_kv_idxs();
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, kv_idxs, il));
}
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
@ -1398,8 +1419,8 @@ ggml_tensor * llm_graph_context::build_attn(
// store to KV cache
{
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, nullptr, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, nullptr, il));
}
const auto & kq_mask = inp->get_kq_mask();
@ -1434,6 +1455,9 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
{
const auto n_kv = mctx_cur->get_base()->get_n_kv();
inp->self_kv_idxs = ggml_new_tensor_1d(ctx0, GGML_TYPE_I64, n_tokens);
ggml_set_input(inp->self_kv_idxs);
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
ggml_set_input(inp->self_kq_mask);
@ -1446,6 +1470,9 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
const auto n_kv = mctx_cur->get_swa()->get_n_kv();
inp->self_kv_idxs_swa = ggml_new_tensor_1d(ctx0, GGML_TYPE_I64, n_tokens);
ggml_set_input(inp->self_kv_idxs_swa);
inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
ggml_set_input(inp->self_kq_mask_swa);

8
src/llama-graph.h
View File

@ -248,8 +248,11 @@ public:
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * get_kv_idxs() const { return self_kv_idxs; }
ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
ggml_tensor * self_kv_idxs = nullptr; // I64 [n_batch]
ggml_tensor * self_kq_mask = nullptr; // F32 [n_kv, n_batch]
ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch]
@ -273,9 +276,14 @@ public:
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * get_kv_idxs() const { return self_kv_idxs; }
ggml_tensor * get_kv_idxs_swa() const { return self_kv_idxs_swa; }
ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; }
ggml_tensor * self_kv_idxs = nullptr; // I64 [n_batch]
ggml_tensor * self_kv_idxs_swa = nullptr; // I64 [n_batch]
ggml_tensor * self_kq_mask = nullptr; // F32 [n_kv, n_batch]
ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch]
ggml_tensor * self_kq_mask_swa = nullptr; // F32 [n_kv, n_batch]

32
src/llama-kv-cache-unified-iswa.cpp
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@ -113,20 +113,20 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads_base = kv_base->prepare(ubatches);
if (heads_base.empty()) {
auto sinfos_base = kv_base->prepare(ubatches);
if (sinfos_base.empty()) {
break;
}
auto heads_swa = kv_swa->prepare(ubatches);
if (heads_swa.empty()) {
auto sinfos_swa = kv_swa->prepare(ubatches);
if (sinfos_swa.empty()) {
break;
}
assert(heads_base.size() == heads_swa.size());
assert(sinfos_base.size() == sinfos_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
} while (false);
// if it fails, try equal split
@ -144,20 +144,20 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads_base = kv_base->prepare(ubatches);
if (heads_base.empty()) {
auto sinfos_base = kv_base->prepare(ubatches);
if (sinfos_base.empty()) {
break;
}
auto heads_swa = kv_swa->prepare(ubatches);
if (heads_swa.empty()) {
auto sinfos_swa = kv_swa->prepare(ubatches);
if (sinfos_swa.empty()) {
break;
}
assert(heads_base.size() == heads_swa.size());
assert(sinfos_base.size() == sinfos_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
} while (false);
// TODO: if we fail again, we should attempt different splitting strategies
@ -220,13 +220,13 @@ llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_swa,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(heads_base), this->ubatches)),
ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(heads_swa), this->ubatches)),
ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(sinfos_base), this->ubatches)),
ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(sinfos_swa), this->ubatches)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}

6
src/llama-kv-cache-unified-iswa.h
View File

@ -74,6 +74,8 @@ private:
class llama_kv_cache_unified_iswa_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
// used for errors
llama_kv_cache_unified_iswa_context(llama_memory_status status);
@ -90,8 +92,8 @@ public:
// used to create a batch processing context from a batch
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
slot_info_vec_t sinfos_base,
slot_info_vec_t sinfos_swa,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_unified_iswa_context();

225
src/llama-kv-cache-unified.cpp
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@ -156,6 +156,13 @@ llama_kv_cache_unified::llama_kv_cache_unified(
const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;
const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) : 0;
if (!supports_set_rows) {
LLAMA_LOG_WARN("%s: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility\n", __func__);
}
}
void llama_kv_cache_unified::clear(bool data) {
@ -353,13 +360,13 @@ llama_memory_context_ptr llama_kv_cache_unified::init_batch(
ubatches.push_back(std::move(ubatch)); // NOLINT
}
auto heads = prepare(ubatches);
if (heads.empty()) {
auto sinfos = prepare(ubatches);
if (sinfos.empty()) {
break;
}
return std::make_unique<llama_kv_cache_unified_context>(
this, std::move(heads), std::move(ubatches));
this, std::move(sinfos), std::move(ubatches));
} while (false);
return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
@ -402,12 +409,13 @@ llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lct
return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo));
}
llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
llama_kv_cache_unified::ubatch_heads res;
llama_kv_cache_unified::slot_info_vec_t llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
llama_kv_cache_unified::slot_info_vec_t res;
struct state {
uint32_t head_old; // old position of the head, before placing the ubatch
uint32_t head_new; // new position of the head, after placing the ubatch
slot_info sinfo; // slot info for the ubatch
llama_kv_cells_unified cells; // copy of the old cells, before placing the ubatch
};
@ -418,26 +426,29 @@ llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::
bool success = true;
for (const auto & ubatch : ubatches) {
// non-continuous slots require support for ggml_set_rows()
const bool cont = supports_set_rows ? false : true;
// only find a suitable slot for the ubatch. don't modify the cells yet
const int32_t head_new = find_slot(ubatch);
if (head_new < 0) {
const auto sinfo_new = find_slot(ubatch, cont);
if (sinfo_new.empty()) {
success = false;
break;
}
// remeber the position that we found
res.push_back(head_new);
res.push_back(sinfo_new);
// store the old state of the cells in the recovery stack
states.push_back({head, (uint32_t) head_new, cells.cp(head_new, ubatch.n_tokens)});
states.push_back({head, sinfo_new, cells.cp(sinfo_new.idxs)});
// now emplace the ubatch
apply_ubatch(head_new, ubatch);
apply_ubatch(sinfo_new, ubatch);
}
// iterate backwards and restore the cells to their original state
for (auto it = states.rbegin(); it != states.rend(); ++it) {
cells.set(it->head_new, it->cells);
cells.set(it->sinfo.idxs, it->cells);
head = it->head_old;
}
@ -539,7 +550,7 @@ bool llama_kv_cache_unified::update(llama_context * lctx, bool do_shift, const d
return updated;
}
int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
llama_kv_cache_unified::slot_info llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch, bool cont) const {
const uint32_t n_tokens = ubatch.n_tokens;
uint32_t head_cur = this->head;
@ -552,7 +563,7 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
if (n_tokens > cells.size()) {
LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
return -1;
return { };
}
if (debug > 0) {
@ -613,17 +624,25 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
}
}
uint32_t n_found = 0;
uint32_t n_tested = 0;
const uint32_t n_test = cont ? n_tokens : 1;
slot_info res;
res.idxs.resize(n_tokens);
while (true) {
if (head_cur + n_tokens > cells.size()) {
if (head_cur + n_test > cells.size()) {
n_tested += cells.size() - head_cur;
head_cur = 0;
continue;
}
bool found = true;
for (uint32_t i = 0; i < n_tokens; i++) {
for (uint32_t i = 0; i < n_test; i++) {
const auto idx = head_cur;
//const llama_pos pos = ubatch.pos[i];
//const llama_seq_id seq_id = ubatch.seq_id[i][0];
@ -633,19 +652,19 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
// - (disabled) mask causally, if the sequence is the same as the one we are inserting
// - mask SWA, using current max pos for that sequence in the cache
// always insert in the cell with minimum pos
bool can_use = cells.is_empty(head_cur + i);
bool can_use = cells.is_empty(idx);
if (!can_use && cells.seq_count(head_cur + i) == 1) {
const llama_pos pos_cell = cells.pos_get(head_cur + i);
if (!can_use && cells.seq_count(idx) == 1) {
const llama_pos pos_cell = cells.pos_get(idx);
// (disabled) causal mask
// note: it's better to purge any "future" tokens beforehand
//if (cells.seq_has(head_cur + i, seq_id)) {
//if (cells.seq_has(idx, seq_id)) {
// can_use = pos_cell >= pos;
//}
if (!can_use) {
const llama_seq_id seq_id_cell = cells.seq_get(head_cur + i);
const llama_seq_id seq_id_cell = cells.seq_get(idx);
// SWA mask
if (is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
@ -654,28 +673,41 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
}
}
if (!can_use) {
found = false;
head_cur += i + 1;
n_tested += i + 1;
head_cur++;
n_tested++;
if (can_use) {
res.idxs[n_found] = idx;
n_found++;
} else {
break;
}
}
if (found) {
if (n_found == n_tokens) {
break;
}
if (cont) {
n_found = 0;
}
if (n_tested >= cells.size()) {
//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
return -1;
return { };
}
}
return head_cur;
// we didn't find a suitable slot - return empty result
if (n_found < n_tokens) {
res.clear();
}
return res;
}
void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) {
void llama_kv_cache_unified::apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch) {
// keep track of the max sequence position that we would overwrite with this ubatch
// for non-SWA cache, this would be always empty
llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
@ -683,22 +715,26 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
seq_pos_max_rm[s] = -1;
}
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
if (!cells.is_empty(head_cur + i)) {
assert(cells.seq_count(head_cur + i) == 1);
assert(ubatch.n_tokens == sinfo.idxs.size());
const llama_seq_id seq_id = cells.seq_get(head_cur + i);
const llama_pos pos = cells.pos_get(head_cur + i);
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
const auto idx = sinfo.idxs[i];
if (!cells.is_empty(idx)) {
assert(cells.seq_count(idx) == 1);
const llama_seq_id seq_id = cells.seq_get(idx);
const llama_pos pos = cells.pos_get(idx);
seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
cells.rm(head_cur + i);
cells.rm(idx);
}
cells.pos_set(head_cur + i, ubatch.pos[i]);
cells.pos_set(idx, ubatch.pos[i]);
for (int32_t s = 0; s < ubatch.n_seq_id[i]; s++) {
cells.seq_add(head_cur + i, ubatch.seq_id[i][s]);
cells.seq_add(idx, ubatch.seq_id[i][s]);
}
}
@ -719,7 +755,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
}
// move the head at the end of the slot
head = head_cur + ubatch.n_tokens;
head = sinfo.idxs.back() + 1;
}
bool llama_kv_cache_unified::get_can_shift() const {
@ -772,47 +808,98 @@ ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint
0);
}
ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const {
ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const {
const int32_t ikv = map_layer_ids.at(il);
auto * k = layers[ikv].k;
const int64_t n_embd_k_gqa = k->ne[0];
const int64_t n_tokens = k_cur->ne[2];
k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
if (kv_idxs && supports_set_rows) {
return ggml_set_rows(ctx, k, k_cur, kv_idxs);
}
// TODO: fallback to old ggml_cpy() method for backwards compatibility
// will be removed when ggml_set_rows() is adopted by all backends
ggml_tensor * k_view = ggml_view_1d(ctx, k,
n_tokens*hparams.n_embd_k_gqa(il),
ggml_row_size(k->type, hparams.n_embd_k_gqa(il))*head_cur);
n_tokens*n_embd_k_gqa,
ggml_row_size(k->type, n_embd_k_gqa)*sinfo.head());
return ggml_cpy(ctx, k_cur, k_view);
}
ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const {
ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const {
const int32_t ikv = map_layer_ids.at(il);
auto * v = layers[ikv].v;
const int64_t n_embd_v_gqa = v->ne[0];
const int64_t n_tokens = v_cur->ne[2];
v_cur = ggml_reshape_2d(ctx, v_cur, hparams.n_embd_v_gqa(il), n_tokens);
v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
if (kv_idxs && supports_set_rows) {
if (!v_trans) {
return ggml_set_rows(ctx, v, v_cur, kv_idxs);
}
// the row becomes a single element
ggml_tensor * v_view = ggml_reshape_3d(ctx, v, 1, v->ne[1], v->ne[0]);
// note: the V cache is transposed when not using flash attention
v_cur = ggml_permute(ctx, ggml_reshape_3d(ctx, v_cur, v_cur->ne[0], 1, v_cur->ne[1]), 2, 0, 1, 3);
// note: we can be more explicit here at the cost of extra cont
// however, above we take advantage that a row of single element is always contiguous regardless of the row stride
//v_cur = ggml_transpose(ctx, v_cur);
//v_cur = ggml_cont_3d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1]);
// we broadcast the KV indices n_embd_v_gqa times
// v [1, n_kv, n_embd_v_gqa]
// v_cur [1, n_tokens, n_embd_v_gqa]
// kv_idxs [n_tokens, 1, 1]
return ggml_set_rows(ctx, v_view, v_cur, kv_idxs);
}
// TODO: fallback to old ggml_cpy() method for backwards compatibility
// will be removed when ggml_set_rows() is adopted by all backends
ggml_tensor * v_view = nullptr;
if (!v_trans) {
v_view = ggml_view_1d(ctx, v,
n_tokens*hparams.n_embd_v_gqa(il),
ggml_row_size(v->type, hparams.n_embd_v_gqa(il))*head_cur);
n_tokens*n_embd_v_gqa,
ggml_row_size(v->type, n_embd_v_gqa)*sinfo.head());
} else {
// note: the V cache is transposed when not using flash attention
v_view = ggml_view_2d(ctx, v, n_tokens, hparams.n_embd_v_gqa(il),
(v->ne[1])*ggml_element_size(v),
(head_cur)*ggml_element_size(v));
v_cur = ggml_transpose(ctx, v_cur);
v_view = ggml_view_2d(ctx, v, n_tokens, n_embd_v_gqa,
(v->ne[1] )*ggml_element_size(v),
(sinfo.head())*ggml_element_size(v));
}
return ggml_cpy(ctx, v_cur, v_view);
}
void llama_kv_cache_unified::set_input_kv_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
if (!supports_set_rows) {
return;
}
const uint32_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
int64_t * data = (int64_t *) dst->data;
for (int64_t i = 0; i < n_tokens; ++i) {
data[i] = sinfo.idxs[i];
}
}
void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
const uint32_t n_tokens = ubatch->n_tokens;
@ -1552,13 +1639,15 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
ubatch.seq_id[i] = &dest_seq_id;
}
const auto head_cur = find_slot(ubatch);
if (head_cur < 0) {
const auto sinfo = find_slot(ubatch, true);
if (sinfo.empty()) {
LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
return false;
}
apply_ubatch(head_cur, ubatch);
apply_ubatch(sinfo, ubatch);
const auto head_cur = sinfo.head();
// keep the head at the old position because we will read the KV data into it in state_read_data()
head = head_cur;
@ -1744,7 +1833,10 @@ llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_stat
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
n_kv = kv->get_size();
head = 0;
sinfos.resize(1);
sinfos[0].idxs.resize(1);
sinfos[0].idxs[0] = 0;
}
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
@ -1759,8 +1851,8 @@ llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_kv_cache_unified::ubatch_heads heads,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) {
llama_kv_cache_unified::slot_info_vec_t sinfos,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sinfos(std::move(sinfos)), ubatches(std::move(ubatches)) {
}
llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
@ -1768,7 +1860,7 @@ llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
bool llama_kv_cache_unified_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
if (++i_cur >= ubatches.size()) {
return false;
}
@ -1785,10 +1877,9 @@ bool llama_kv_cache_unified_context::apply() {
return true;
}
kv->apply_ubatch(heads[i_next], ubatches[i_next]);
kv->apply_ubatch(sinfos[i_cur], ubatches[i_cur]);
n_kv = kv->get_n_kv();
head = heads[i_next];
return true;
}
@ -1800,7 +1891,7 @@ llama_memory_status llama_kv_cache_unified_context::get_status() const {
const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
return ubatches[i_cur];
}
uint32_t llama_kv_cache_unified_context::get_n_kv() const {
@ -1815,18 +1906,22 @@ ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t
return kv->get_v(ctx, il, n_kv);
}
ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
return kv->cpy_k(ctx, k_cur, il, head);
ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il) const {
return kv->cpy_k(ctx, k_cur, kv_idxs, il, sinfos[i_cur]);
}
ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
return kv->cpy_v(ctx, v_cur, il, head);
ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il) const {
return kv->cpy_v(ctx, v_cur, kv_idxs, il, sinfos[i_cur]);
}
void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
kv->set_input_k_shift(dst);
}
void llama_kv_cache_unified_context::set_input_kv_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
kv->set_input_kv_idxs(dst, ubatch, sinfos[i_cur]);
}
void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
kv->set_input_kq_mask(dst, ubatch, causal_attn);
}

71
src/llama-kv-cache-unified.h
View File

@ -24,8 +24,6 @@ public:
// this callback is used to filter out layers that should not be included in the cache
using layer_filter_cb = std::function<bool(int32_t il)>;
using ubatch_heads = std::vector<uint32_t>;
struct defrag_info {
bool empty() const {
return ids.empty();
@ -37,6 +35,30 @@ public:
std::vector<uint32_t> ids;
};
struct slot_info {
// data for ggml_set_rows
using idx_vec_t = std::vector<uint32_t>;
idx_vec_t idxs;
uint32_t head() const {
return idxs[0];
}
bool empty() const {
return idxs.empty();
}
void clear() {
idxs.clear();
}
// TODO: implement
//std::vector<idx_vec_t> seq_idxs;
};
using slot_info_vec_t = std::vector<slot_info>;
llama_kv_cache_unified(
const llama_model & model,
layer_filter_cb && filter,
@ -102,30 +124,32 @@ public:
ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
// store k_cur and v_cur in the cache based on the provided head location
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const;
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const;
//
// preparation API
//
// find places for the provided ubatches in the cache, returns the head locations
// find places for the provided ubatches in the cache, returns the slot infos
// return empty vector on failure
ubatch_heads prepare(const std::vector<llama_ubatch> & ubatches);
slot_info_vec_t prepare(const std::vector<llama_ubatch> & ubatches);
bool update(llama_context * lctx, bool do_shift, const defrag_info & dinfo);
// return the cell position where we can insert the ubatch
// return -1 on failure to find a contiguous slot of kv cells
int32_t find_slot(const llama_ubatch & ubatch) const;
// find a slot of kv cells that can hold the ubatch
// if cont == true, then the slot must be continuous
// return empty slot_info on failure
slot_info find_slot(const llama_ubatch & ubatch, bool cont) const;
// emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
// emplace the ubatch context into slot: [sinfo.idxs[0...ubatch.n_tokens - 1]]
void apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch);
//
// set_input API
//
void set_input_kv_idxs (ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
void set_input_k_shift (ggml_tensor * dst) const;
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
@ -157,8 +181,13 @@ private:
// SWA
const uint32_t n_swa = 0;
// env: LLAMA_KV_CACHE_DEBUG
int debug = 0;
// env: LLAMA_SET_ROWS (temporary)
// ref: https://github.com/ggml-org/llama.cpp/pull/14285
int supports_set_rows = false;
const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
std::vector<ggml_context_ptr> ctxs;
@ -211,8 +240,8 @@ private:
class llama_kv_cache_unified_context : public llama_memory_context_i {
public:
// some shorthands
using ubatch_heads = llama_kv_cache_unified::ubatch_heads;
using defrag_info = llama_kv_cache_unified::defrag_info;
using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
using defrag_info = llama_kv_cache_unified::defrag_info;
// used for errors
llama_kv_cache_unified_context(llama_memory_status status);
@ -231,7 +260,7 @@ public:
// used to create a batch procesing context from a batch
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
ubatch_heads heads,
slot_info_vec_t sinfos,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_unified_context();
@ -257,11 +286,12 @@ public:
ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
// store k_cur and v_cur in the cache based on the provided head location
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il) const;
ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il) const;
void set_input_k_shift(ggml_tensor * dst) const;
void set_input_kv_idxs (ggml_tensor * dst, const llama_ubatch * ubatch) const;
void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
@ -283,10 +313,10 @@ private:
// batch processing context
//
// the index of the next ubatch to process
size_t i_next = 0;
// the index of the cur ubatch to process
size_t i_cur = 0;
ubatch_heads heads;
slot_info_vec_t sinfos;
std::vector<llama_ubatch> ubatches;
@ -297,7 +327,4 @@ private:
// a heuristic, to avoid attending the full cache if it is not yet utilized
// as the cache gets filled, the benefit from this heuristic disappears
int32_t n_kv;
// the beginning of the current slot in which the ubatch will be inserted
int32_t head;
};

72
src/llama-kv-cells.h
View File

@ -105,10 +105,30 @@ public:
res.resize(n);
for (uint32_t j = 0; j < n; ++j) {
res.pos[j] = pos[i + j];
res.seq[j] = seq[i + j];
const auto idx = i + j;
assert(shift[i + j] == 0);
res.pos[j] = pos[idx];
res.seq[j] = seq[idx];
assert(shift[idx] == 0);
}
return res;
}
// copy the state of cells [idxs[0], idxs[1], ..., idxs[idxs.size() - 1])
llama_kv_cells_unified cp(const std::vector<uint32_t> & idxs) const {
llama_kv_cells_unified res;
res.resize(idxs.size());
for (uint32_t j = 0; j < idxs.size(); ++j) {
const auto idx = idxs[j];
res.pos[j] = pos[idx];
res.seq[j] = seq[idx];
assert(shift[idx] == 0);
}
return res;
@ -119,26 +139,58 @@ public:
assert(i + other.pos.size() <= pos.size());
for (uint32_t j = 0; j < other.pos.size(); ++j) {
if (pos[i + j] == -1 && other.pos[j] != -1) {
const auto idx = i + j;
if (pos[idx] == -1 && other.pos[j] != -1) {
used.insert(i + j);
}
if (pos[i + j] != -1 && other.pos[j] == -1) {
if (pos[idx] != -1 && other.pos[j] == -1) {
used.erase(i + j);
}
if (pos[i + j] != -1) {
if (pos[idx] != -1) {
seq_pos_rm(i + j);
}
pos[i + j] = other.pos[j];
seq[i + j] = other.seq[j];
pos[idx] = other.pos[j];
seq[idx] = other.seq[j];
if (pos[i + j] != -1) {
if (pos[idx] != -1) {
seq_pos_add(i + j);
}
assert(shift[i + j] == 0);
assert(shift[idx] == 0);
}
}
// set the state of cells [idxs[0], idxs[1], ..., idxs[idxs.size() - 1])
void set(const std::vector<uint32_t> & idxs, const llama_kv_cells_unified & other) {
assert(idxs.size() == other.pos.size());
for (uint32_t j = 0; j < other.pos.size(); ++j) {
const auto idx = idxs[j];
if (pos[idx] == -1 && other.pos[j] != -1) {
used.insert(idx);
}
if (pos[idx] != -1 && other.pos[j] == -1) {
used.erase(idx);
}
if (pos[idx] != -1) {
seq_pos_rm(idx);
}
pos[idx] = other.pos[j];
seq[idx] = other.seq[j];
if (pos[idx] != -1) {
seq_pos_add(idx);
}
assert(shift[idx] == 0);
}
}

4
src/llama-memory-hybrid.cpp
View File

@ -195,11 +195,11 @@ llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
std::vector<uint32_t> heads_attn,
slot_info_vec_t sinfos_attn,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}

4
src/llama-memory-hybrid.h
View File

@ -92,6 +92,8 @@ private:
class llama_memory_hybrid_context : public llama_memory_context_i {
public:
using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
// init failure
explicit llama_memory_hybrid_context(llama_memory_status status);
@ -107,7 +109,7 @@ public:
// init success
llama_memory_hybrid_context(
llama_memory_hybrid * mem,
std::vector<uint32_t> heads_attn,
slot_info_vec_t sinfos_attn,
std::vector<llama_ubatch> ubatches);
~llama_memory_hybrid_context() = default;