#include "llama-model.h"
#include "llama-kv-cache.h"
-#include <cassert>
#include <cstring>
#include <stdexcept>
#include <cinttypes>
-#include <cmath>
//
// llama_context
}
// init the memory module
- // TODO: for now, always create a unified KV cache
if (!hparams.vocab_only) {
- kv_self.reset(static_cast<llama_kv_cache_unified *>(model.create_memory()));
+ llama_memory_params params_mem = {
+ /*.type_k =*/ params.type_k,
+ /*.type_v =*/ params.type_v,
+ };
- LLAMA_LOG_DEBUG("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
-
- cparams.n_ctx = GGML_PAD(cparams.n_ctx, kv_self->get_padding(cparams));
-
- LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx);
-
- uint32_t kv_size = cparams.n_ctx;
- ggml_type type_k = params.type_k;
- ggml_type type_v = params.type_v;
-
- if (llama_model_is_recurrent(&model)) {
- // Mamba needs at least as many KV cells as there are sequences kept at any time
- kv_size = std::max((uint32_t) 1, params.n_seq_max);
- // it's probably best to keep as much precision as possible for the states
- type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
- type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
- }
-
- GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
- GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
-
- if (!kv_self->init(model, cparams, type_k, type_v, kv_size, cparams.offload_kqv)) {
- throw std::runtime_error("failed to initialize self-attention cache");
- }
-
- {
- const size_t memory_size_k = kv_self->size_k_bytes();
- const size_t memory_size_v = kv_self->size_v_bytes();
-
- LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
- (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
- 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));
- }
+ memory.reset(model.create_memory(params_mem, cparams));
}
// init backends
int n_nodes_tg = -1;
// simulate full KV cache
- kv_self->n = kv_self->size;
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
+ kv_self->set_full();
cross.v_embd.clear();
return model;
}
+const llama_cparams & llama_context::get_cparams() const {
+ return cparams;
+}
+
+ggml_backend_sched_t llama_context::get_sched() const {
+ return sched.get();
+}
+
+ggml_context * llama_context::get_ctx_compute() const {
+ return ctx_compute.get();
+}
+
uint32_t llama_context::n_ctx() const {
return cparams.n_ctx;
}
}
llama_kv_cache * llama_context::get_kv_self() {
- return kv_self.get();
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+ return kv_self;
}
const llama_kv_cache * llama_context::get_kv_self() const {
- return kv_self.get();
-}
-
-ggml_tensor * llama_context::build_rope_shift(
- ggml_context * ctx0,
- ggml_tensor * cur,
- ggml_tensor * shift,
- ggml_tensor * factors,
- float freq_base,
- float freq_scale) const {
- const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;
-
- const auto & yarn_ext_factor = cparams.yarn_ext_factor;
- const auto & yarn_beta_fast = cparams.yarn_beta_fast;
- const auto & yarn_beta_slow = cparams.yarn_beta_slow;
-
- const auto & hparams = model.hparams;
-
- const auto & n_rot = hparams.n_rot;
- const auto & rope_type = hparams.rope_type;
-
- // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly.
- // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
- const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor;
-
- ggml_tensor * tmp;
-
- if (ggml_is_quantized(cur->type)) {
- // dequantize to f32 -> RoPE -> quantize back
- tmp = ggml_cast(ctx0, cur, GGML_TYPE_F32);
-
- tmp = ggml_rope_ext(ctx0, tmp,
- shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow);
-
- tmp = ggml_cpy(ctx0, tmp, cur);
- } else {
- // we rotate only the first n_rot dimensions
- tmp = ggml_rope_ext_inplace(ctx0, cur,
- shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
- yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow);
- }
-
- return tmp;
-}
-
-class llm_graph_input_k_shift : public llm_graph_input_i {
-public:
- llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
- virtual ~llm_graph_input_k_shift() = default;
-
- void set_input(const llama_ubatch * ubatch) override;
-
- ggml_tensor * k_shift; // I32 [kv_size]
-
- const llama_kv_cache_unified * kv_self;
-};
-
-void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) {
- GGML_UNUSED(ubatch);
-
- if (k_shift) {
- assert(ggml_backend_buffer_is_host(k_shift->buffer));
-
- int32_t * data = (int32_t *) k_shift->data;
-
- for (uint32_t i = 0; i < kv_self->size; ++i) {
- data[i] = kv_self->cells[i].delta;
- }
- }
-}
-
-llm_graph_result_ptr llama_context::build_kv_self_shift(
- ggml_context * ctx0,
- ggml_cgraph * gf) const {
- auto res = std::make_unique<llm_graph_result>();
-
- const auto & hparams = model.hparams;
-
- const auto & n_layer = hparams.n_layer;
-
- const auto & n_embd_head_k = hparams.n_embd_head_k;
- //const auto & n_embd_head_v = hparams.n_embd_head_v;
-
- //GGML_ASSERT(kv_self->size == n_ctx);
-
- auto inp = std::make_unique<llm_graph_input_k_shift>(kv_self.get());
-
- inp->k_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, cparams.n_ctx);
- ggml_set_input(inp->k_shift);
-
- for (uint32_t il = 0; il < n_layer; ++il) {
- const int64_t n_head_kv = hparams.n_head_kv(il);
- const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
-
- const bool is_swa = hparams.is_swa(il);
-
- // note: the swa rope params could become part of the cparams in the future
- // if we decide to make them configurable, like the non-sliding ones
- const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
- const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale;
-
- ggml_tensor * rope_factors = kv_self->cbs.get_rope_factors(n_ctx_per_seq(), il);
-
- ggml_tensor * k =
- ggml_view_3d(ctx0, kv_self->k_l[il],
- n_embd_head_k, n_head_kv, kv_self->size,
- ggml_row_size(kv_self->k_l[il]->type, n_embd_head_k),
- ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa),
- 0);
-
- ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l);
-
- ggml_build_forward_expand(gf, cur);
- }
-
- res->add_input(std::move(inp));
-
- return res;
-}
-
-llm_graph_result_ptr llama_context::build_kv_self_defrag(
- ggml_context * ctx0,
- ggml_cgraph * gf) const {
- auto res = std::make_unique<llm_graph_result>();
-
- const auto & hparams = model.hparams;
-
- const auto & ids = kv_self->defrag_info.ids;
-
-#if 0
- // CPU defrag
- //
- // TODO: optimizations are possible:
- // - multiple threads
- // - avoid copying to the host memory when already there
- //
- // likely not worth the effort, as we have ggml_graph based defrag
- //
-
- const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
- const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
-
- const uint32_t kv_size = size;
-
- std::vector<uint8_t> buf_k;
- std::vector<uint8_t> buf_v;
-
- for (uint32_t il = 0; il < n_layer; ++il) {
- const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
- const size_t k_size = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size);
-
- const size_t v_size_el = ggml_type_size(v_l[il]->type);
- const size_t v_size = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size);
-
- buf_k.resize(k_size);
- buf_v.resize(v_size);
-
- ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size());
- ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size());
-
- // batch move [i, i+nm) to [id, id+nm)
- // note: cells can move only to a lower index
- for (uint32_t i = 0; i < n_kv; ++i) {
- const uint32_t id = ids[i];
-
- if (i == id || id == n_kv) {
- continue;
- }
-
- uint32_t nm = 1;
-
- while (i + nm < n_kv && ids[i + nm] == id + nm) {
- nm++;
- }
-
- // move keys
- {
- const int64_t os = i*k_size_row;
- const int64_t od = id*k_size_row;
-
- memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
- }
-
- // move values (note: they are transposed)
- {
- const int64_t os = i;
- const int64_t od = id;
-
- for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
- memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
- }
- }
-
- i += nm - 1;
- }
-
- ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size());
- ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size());
- }
-#else
- for (uint32_t i = 0; i < ids.size(); ++i) {
- const uint32_t id = ids[i];
-
- if (i == id || id == ids.size()) {
- continue;
- }
-
- uint32_t nm = 1;
-
- while (i + nm < ids.size() && ids[i + nm] == id + nm) {
- nm++;
- }
-
- for (uint32_t il = 0; il < hparams.n_layer; ++il) { // NOLINT
- const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
- const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
-
- ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self->k_l[il],
- n_embd_k_gqa, nm,
- ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa),
- ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa*i));
-
- ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self->k_l[il],
- n_embd_k_gqa, nm,
- ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa),
- ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa*id));
-
- ggml_tensor * view_v_src;
- ggml_tensor * view_v_dst;
-
- if (cparams.flash_attn) {
- // NOTE: the V cache is not transposed when using flash attention
- view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il],
- n_embd_v_gqa, nm,
- ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa),
- ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa*i));
-
- view_v_dst = ggml_view_2d(ctx0, kv_self->v_l[il],
- n_embd_v_gqa, nm,
- ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa),
- ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa*id));
- } else {
- view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il],
- nm, n_embd_v_gqa,
- ggml_row_size(kv_self->v_l[il]->type, kv_self->size),
- ggml_row_size(kv_self->v_l[il]->type, i));
-
- view_v_dst = ggml_view_2d(ctx0, kv_self->v_l[il],
- nm, n_embd_v_gqa,
- ggml_row_size(kv_self->v_l[il]->type, kv_self->size),
- ggml_row_size(kv_self->v_l[il]->type, id));
- }
-
- ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
- ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
- }
-
- i += nm - 1;
- }
-
- //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
-#endif
-
- return res;
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+ return kv_self;
}
void llama_context::kv_self_update() {
- auto & kv = kv_self;
-
bool need_reserve = false;
- if (kv->has_shift) {
- if (!kv->get_can_shift()) {
- GGML_ABORT("The current context does not support K-shift");
- }
-
- LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__);
-
- // apply K-shift if needed
- if (model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
- ggml_backend_sched_reset(sched.get());
-
- auto * gf = graph_init();
-
- auto res = build_kv_self_shift(ctx_compute.get(), gf);
-
- ggml_backend_sched_alloc_graph(sched.get(), gf);
-
- res->set_inputs(nullptr);
-
- graph_compute(gf, false);
-
- need_reserve = true;
- }
-
- {
- kv->has_shift = false;
-
- for (uint32_t i = 0; i < kv->size; ++i) {
- kv->cells[i].delta = 0;
- }
- }
- }
-
- // defragment the KV cache if needed
- if (kv->do_defrag) {
- LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
-
- if (kv->defrag_prepare(graph_max_nodes())) {
- ggml_backend_sched_reset(sched.get());
-
- auto * gf = graph_init();
-
- auto res = build_kv_self_defrag(ctx_compute.get(), gf);
-
- ggml_backend_sched_alloc_graph(sched.get(), gf);
-
- res->set_inputs(nullptr);
-
- graph_compute(gf, false);
-
- need_reserve = true;
- }
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
- kv->do_defrag = false;
- }
+ need_reserve = kv_self->update(*this);
// reserve a worst case graph if needed
if (need_reserve) {
uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
// simulate full KV cache
- kv_self->n = kv_self->size;
+ kv_self->set_full();
llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
}
float * llama_context::get_logits() {
- // reorder logits for backward compatibility
- output_reorder();
-
return logits;
}
}
float * llama_context::get_embeddings() {
- // reorder embeddings for backward compatibility
- output_reorder();
-
return embd;
}
}
// temporary allocate memory for the input batch if needed
- // TODO: this is incorrect for multiple sequences because pos_max() is the maximum across all sequences
- llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->pos_max() + 1);
+ // note: during encode, we always pass the full sequence starting from pos = 0
+ llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : 0);
const llama_batch & batch = batch_allocr.batch;
const int32_t n_tokens = batch.n_tokens;
const int64_t n_embd = hparams.n_embd;
- sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
+ llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);
const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
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 pos_max() is the maximum across all sequences
- llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->pos_max() + 1);
+ // 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);
const llama_batch & batch = batch_allocr.batch;
const int64_t n_tokens_all = batch.n_tokens;
const int64_t n_embd = hparams.n_embd;
- llama_kv_cache_guard kv_guard(kv_self.get());
+ llama_kv_cache_guard kv_guard(kv_self);
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
n_outputs_all = 1;
}
- const bool logits_all = n_outputs_all == n_tokens_all;
-
- sbatch.from_batch(batch, n_embd,
- /* simple_split */ !kv_self->recurrent,
- /* logits_all */ logits_all);
+ llama_sbatch sbatch = kv_self->sbatch_init(batch, /* logits_all */ n_outputs_all == n_tokens_all);
// reserve output buffer
if (output_reserve(n_outputs_all) < n_outputs_all) {
int64_t n_outputs_prev = 0;
while (sbatch.n_tokens > 0) {
- llama_ubatch ubatch = llama_ubatch();
-
- const auto & n_ubatch = cparams.n_ubatch;
-
- if (kv_self->recurrent) {
- if (embd_pooled) {
- // Pooled embeddings cannot be split across ubatches (yet)
- ubatch = sbatch.split_seq(cparams.n_ubatch);
- } else {
- // recurrent model architectures are easier to implement
- // with equal-length sequences
- ubatch = sbatch.split_equal(cparams.n_ubatch);
- }
- } else {
- ubatch = sbatch.split_simple(n_ubatch);
- }
+ llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
// count the outputs in this u_batch
{
}
// find KV slot
- {
- if (!kv_self->find_slot(ubatch)) {
- LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens);
+ if (!kv_self->find_slot(ubatch)) {
+ LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens);
- return 1;
- }
-
- if (!kv_self->recurrent) {
- // 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
- const uint32_t pad = kv_self->get_padding(cparams);
- kv_self->n = std::min(kv_self->size, std::max(pad, GGML_PAD(kv_self->cell_max(), pad)));
- }
+ return 1;
}
- //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self->n, kv_self->used, kv_self->head);
-
ggml_backend_sched_reset(sched.get());
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
{
bool sorted_output = true;
- GGML_ASSERT(sbatch.out_ids.size() == (size_t) n_outputs_all);
+ auto & out_ids = sbatch.out_ids;
+
+ GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all);
for (int64_t i = 0; i < n_outputs_all; ++i) {
- int64_t out_id = sbatch.out_ids[i];
+ int64_t out_id = out_ids[i];
output_ids[out_id] = i;
if (out_id != i) {
sorted_output = false;
}
}
- if (sorted_output) {
- sbatch.out_ids.clear();
+ // make the outputs have the same order they had in the user-provided batch
+ // note: this is mostly relevant for recurrent models atm
+ if (!sorted_output) {
+ const uint32_t n_vocab = model.vocab.n_tokens();
+ const uint32_t n_embd = model.hparams.n_embd;
+
+ GGML_ASSERT((size_t) n_outputs == out_ids.size());
+
+ // TODO: is there something more efficient which also minimizes swaps?
+ // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
+ for (int32_t i = 0; i < n_outputs - 1; ++i) {
+ int32_t j_min = i;
+ for (int32_t j = i + 1; j < n_outputs; ++j) {
+ if (out_ids[j] < out_ids[j_min]) {
+ j_min = j;
+ }
+ }
+ if (j_min == i) { continue; }
+ std::swap(out_ids[i], out_ids[j_min]);
+ if (logits_size > 0) {
+ for (uint32_t k = 0; k < n_vocab; k++) {
+ std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
+ }
+ }
+ if (embd_size > 0) {
+ for (uint32_t k = 0; k < n_embd; k++) {
+ std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
+ }
+ }
+ }
+ std::fill(output_ids.begin(), output_ids.end(), -1);
+ for (int32_t i = 0; i < n_outputs; ++i) {
+ output_ids[out_ids[i]] = i;
+ }
}
}
//synchronize();
// decide if we need to defrag the kv cache
- if (cparams.causal_attn && cparams.defrag_thold > 0.0f) {
- // - do not defrag small contexts (i.e. < 2048 tokens)
- // - count the padding towards the number of used tokens
- const float fragmentation = kv_self->n >= 2048 ? std::max(0.0f, 1.0f - float(kv_self->used + kv_self->get_padding(cparams))/float(kv_self->n)) : 0.0f;
-
- // queue defragmentation for next llama_kv_cache_update
- if (fragmentation > cparams.defrag_thold) {
- LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation);
-
- kv_self->defrag();
- }
+ if (cparams.defrag_thold > 0.0f) {
+ kv_self->defrag_sched(cparams.defrag_thold);
}
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
return n_outputs_max;
}
-void llama_context::output_reorder() {
- auto & out_ids = sbatch.out_ids;
- if (!out_ids.empty()) {
- const uint32_t n_vocab = model.vocab.n_tokens();
- const uint32_t n_embd = model.hparams.n_embd;
-
- GGML_ASSERT((size_t) n_outputs == out_ids.size());
-
- // TODO: is there something more efficient which also minimizes swaps?
- // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
- for (int32_t i = 0; i < n_outputs - 1; ++i) {
- int32_t j_min = i;
- for (int32_t j = i + 1; j < n_outputs; ++j) {
- if (out_ids[j] < out_ids[j_min]) {
- j_min = j;
- }
- }
- if (j_min == i) { continue; }
- std::swap(out_ids[i], out_ids[j_min]);
- if (logits_size > 0) {
- for (uint32_t k = 0; k < n_vocab; k++) {
- std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
- }
- }
- if (embd_size > 0) {
- for (uint32_t k = 0; k < n_embd; k++) {
- std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
- }
- }
- }
- std::fill(output_ids.begin(), output_ids.end(), -1);
- for (int32_t i = 0; i < n_outputs; ++i) {
- output_ids[out_ids[i]] = i;
- }
- out_ids.clear();
- }
-}
-
//
// graph
//
/*.backend_cpu =*/ backend_cpu,
/*.cvec =*/ &cvec,
/*.loras =*/ &loras,
- /*.memory =*/ kv_self.get(),
+ /*.memory =*/ memory.get(),
/*.cross =*/ &cross,
/*.n_outputs =*/ n_outputs,
/*.cb =*/ graph_get_cb(),
{
LLAMA_LOG_DEBUG("%s: - writing output ids\n", __func__);
- output_reorder();
-
const auto n_outputs = this->n_outputs;
const auto & output_ids = this->output_ids;
}
LLAMA_LOG_DEBUG("%s: - writing KV self\n", __func__);
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
kv_self->state_write(io);
return io.n_bytes();
}
LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__);
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
kv_self->state_read(io);
return io.n_bytes();
size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id) {
GGML_UNUSED(seq_id);
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
kv_self->state_write(io, seq_id);
return io.n_bytes();
size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id) {
GGML_UNUSED(seq_id);
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
kv_self->state_read(io, seq_id);
return io.n_bytes();
llama_seq_id seq_id_dst,
llama_pos p0,
llama_pos p1) {
- return llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1);
+ llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1);
}
void llama_kv_self_seq_cp(
return;
}
- return kv->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+ kv->seq_cp(seq_id_src, seq_id_dst, p0, p1);
}
// deprecated
void llama_kv_cache_seq_keep(
llama_context * ctx,
llama_seq_id seq_id) {
- return llama_kv_self_seq_keep(ctx, seq_id);
+ llama_kv_self_seq_keep(ctx, seq_id);
}
void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) {
return;
}
- return kv->seq_keep(seq_id);
+ kv->seq_keep(seq_id);
}
// deprecated
llama_pos p0,
llama_pos p1,
llama_pos delta) {
- return llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta);
+ llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta);
}
void llama_kv_self_seq_add(
return;
}
- return kv->seq_add(seq_id, p0, p1, delta);
+ kv->seq_add(seq_id, p0, p1, delta);
}
// deprecated
llama_pos p0,
llama_pos p1,
int d) {
- return llama_kv_self_seq_div(ctx, seq_id, p0, p1, d);
+ llama_kv_self_seq_div(ctx, seq_id, p0, p1, d);
}
void llama_kv_self_seq_div(
return;
}
- return kv->seq_div(seq_id, p0, p1, d);
+ kv->seq_div(seq_id, p0, p1, d);
}
// deprecated
// deprecated
void llama_kv_cache_defrag(llama_context * ctx) {
- return llama_kv_self_defrag(ctx);
+ llama_kv_self_defrag(ctx);
}
void llama_kv_self_defrag(llama_context * ctx) {
return;
}
- return kv->defrag();
+ // force defrag
+ kv->defrag_sched(-1.0f);
}
// deprecated
#include "llama-batch.h"
#include "llama-cparams.h"
#include "llama-model.h"
+#include "llama-context.h"
#include <algorithm>
#include <cassert>
+#include <cmath>
#include <limits>
#include <map>
#include <stdexcept>
-llama_kv_cache_unified::llama_kv_cache_unified(const llama_hparams & hparams, callbacks cbs) : hparams(hparams), cbs(std::move(cbs)) {
+//
+// llama_kv_cache_unified
+//
+
+uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) {
+ // the FA kernels require padding to avoid extra runtime boundary checks
+ return cparams.flash_attn ? 256u : 32u;
}
-bool llama_kv_cache_unified::init(
+llama_kv_cache_unified::llama_kv_cache_unified(
const llama_model & model,
- const llama_cparams & cparams,
ggml_type type_k,
ggml_type type_v,
+ bool v_trans,
+ bool offload,
uint32_t kv_size,
- bool offload) {
+ uint32_t padding) : model(model), hparams(model.hparams), v_trans(v_trans), padding(padding) {
const int32_t n_layer = hparams.n_layer;
has_shift = false;
+ can_shift = true;
- recurrent = llama_model_is_recurrent(&model);
- v_trans = !recurrent && !cparams.flash_attn;
- can_shift = !recurrent;
+ LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d, padding = %d\n",
+ __func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, can_shift, padding);
- LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d\n",
- __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, can_shift);
+ GGML_ASSERT(kv_size % padding == 0 && "kv_size must be a multiple of padding");
head = 0;
size = kv_size;
const char * dev_name = "CPU";
- ggml_backend_buffer_type_t buft;
+ ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type();
+
if (offload) {
auto * dev = model.dev_layer(i);
buft = ggml_backend_dev_buffer_type(dev);
dev_name = ggml_backend_dev_name(dev);
- } else {
- buft = ggml_backend_cpu_buffer_type();
}
- LLAMA_LOG_DEBUG("%s: layer %3d: n_embd_k_gqa = %d, n_embd_v_gqa = %d, dev = %s\n", __func__,
- i, n_embd_k_gqa, n_embd_v_gqa, dev_name);
+ LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, i, dev_name);
ggml_context * ctx = ctx_for_buft(buft);
if (!ctx) {
- LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
- return false;
+ throw std::runtime_error("failed to create ggml context for kv cache");
}
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
if (!buf) {
- LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
- return false;
+ throw std::runtime_error("failed to allocate buffer for kv cache");
}
ggml_backend_buffer_clear(buf, 0);
LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
bufs.emplace_back(buf);
}
- 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;
-}
-
-size_t llama_kv_cache_unified::total_size() const {
- size_t size = 0;
- for (const auto & buf : bufs) {
- size += ggml_backend_buffer_get_size(buf.get());
- }
-
- return size;
-}
+ {
+ const size_t memory_size_k = size_k_bytes();
+ const size_t memory_size_v = size_v_bytes();
-llama_pos llama_kv_cache_unified::pos_max() const {
- llama_pos pos_max = -1;
- for (const auto & cell : cells) {
- pos_max = std::max(pos_max, cell.pos);
+ LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
+ (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
+ 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));
}
-
- return pos_max;
}
void llama_kv_cache_unified::clear() {
for (int32_t i = 0; i < (int32_t) size; ++i) {
cells[i].pos = -1;
cells[i].seq_id.clear();
- cells[i].src = -1;
- cells[i].tail = -1;
}
head = 0;
used = 0;
p1 = std::numeric_limits<llama_pos>::max();
}
- // models like Mamba or RWKV can't have a state partially erased
- if (recurrent) {
- if (seq_id >= (int64_t) size) {
- // could be fatal
- return false;
- }
- if (0 <= seq_id) {
- int32_t & tail_id = cells[seq_id].tail;
- if (tail_id >= 0) {
- const llama_kv_cell & cell = cells[tail_id];
- // partial intersection is invalid
- if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
- return false;
- }
- // invalidate tails which will be cleared
- if (p0 <= cell.pos && cell.pos < p1) {
- tail_id = -1;
- }
- }
- } else {
- // seq_id is negative, then the range should include everything or nothing
- if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
- return false;
- }
- }
-
- return true;
- }
-
for (uint32_t i = 0; i < size; ++i) {
if (cells[i].pos >= p0 && cells[i].pos < p1) {
if (seq_id < 0) {
}
cells[i].pos = -1;
- cells[i].src = -1;
if (new_head == size) {
new_head = i;
p1 = std::numeric_limits<llama_pos>::max();
}
- if (recurrent) {
- if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) {
- llama_kv_cell & tail_src = cells[seq_id_src];
- llama_kv_cell & tail_dst = cells[seq_id_dst];
- if (tail_dst.tail >= 0) {
- // clear destination seq_id if it wasn't empty
- llama_kv_cell & cell_dst = cells[tail_dst.tail];
-
- cell_dst.seq_id.erase(seq_id_dst);
- tail_dst.tail = -1;
- if (cell_dst.seq_id.empty()) {
- cell_dst.pos = -1;
- cell_dst.delta = -1;
- cell_dst.src = -1;
- used -= 1;
- }
- }
- if (tail_src.tail >= 0) {
- llama_kv_cell & cell_src = cells[tail_src.tail];
-
- cell_src.seq_id.insert(seq_id_dst);
- tail_dst.tail = tail_src.tail;
- }
- }
-
- return;
- }
-
// otherwise, this is the KV of a Transformer-like model
head = 0;
uint32_t new_head = size;
for (uint32_t i = 0; i < size; ++i) {
- if (recurrent && (llama_seq_id) i != seq_id) {
- cells[i].tail = -1;
- }
-
if (!cells[i].has_seq_id(seq_id)) {
if (cells[i].pos >= 0) {
used--;
}
cells[i].pos = -1;
- cells[i].src = -1;
cells[i].seq_id.clear();
if (new_head == size){
return;
}
- if (recurrent) {
- // for Mamba-like or RWKV models, only the pos needs to be shifted
- if (0 <= seq_id && seq_id < (int64_t) size) {
- const int32_t tail_id = cells[seq_id].tail;
- if (tail_id >= 0) {
- llama_kv_cell & cell = cells[tail_id];
- if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
- cell.pos += delta;
- }
- }
- }
- return;
- }
-
for (uint32_t i = 0; i < size; ++i) {
if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) {
has_shift = true;
return;
}
- if (recurrent) {
- // for Mamba-like or RWKV models, only the pos needs to be changed
- if (0 <= seq_id && seq_id < (int64_t) size) {
- const int32_t tail_id = cells[seq_id].tail;
- if (tail_id >= 0) {
- llama_kv_cell & cell = cells[tail_id];
- if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
- cell.pos /= d;
- }
- }
- }
-
- return;
- }
-
for (uint32_t i = 0; i < size; ++i) {
if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) {
has_shift = true;
return result;
}
-void llama_kv_cache_unified::defrag() {
- if (!recurrent) {
- do_defrag = true;
- }
-}
-
void llama_kv_cache_unified::restore() {
if (pending.ranges.empty()) {
return;
}
- // TODO: tmp - move to llama_kv_cache_recurrent
- if (recurrent) {
- seq_rm(-1, -1, -1);
- return;
- }
-
uint32_t new_head = size;
for (auto & range : pending.ranges) {
}
cells[i].pos = -1;
- cells[i].src = -1;
}
new_head = std::min(new_head, range.c0);
}
void llama_kv_cache_unified::commit() {
- // TODO: tmp - move to llama_kv_cache_recurrent
- if (recurrent) {
- return;
- }
-
if (pending.ranges.empty()) {
LLAMA_LOG_WARN("%s: no pending KV cache updates to commit - might indicate a bug (ref: %s)\n",
__func__, "https://github.com/ggml-org/llama.cpp/pull/12695");
pending.ranges.clear();
}
-bool llama_kv_cache_unified::get_can_shift() const {
- return can_shift;
-}
+bool llama_kv_cache_unified::update(llama_context & lctx) {
+ bool need_reserve = false;
-bool llama_kv_cache_unified::find_slot(
- const llama_ubatch & ubatch) {
- const uint32_t n_tokens = ubatch.n_tokens;
- const uint32_t n_seqs = ubatch.n_seqs;
- const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
+ auto * sched = lctx.get_sched();
- // if we have enough unused cells before the current head ->
- // better to start searching from the beginning of the cache, hoping to fill it
- if (head > used + 2*ubatch.n_tokens) {
- head = 0;
- }
+ if (has_shift) {
+ if (!get_can_shift()) {
+ GGML_ABORT("The current KV cache / model configuration does not support K-shift");
+ }
- if (recurrent) {
- // For recurrent state architectures (like Mamba or RWKV),
- // each cache cell can store the state for a whole sequence.
- // A slot should be always be contiguous.
+ LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__);
- // can only process batches with an equal number of new tokens in each sequence
- GGML_ASSERT(ubatch.equal_seqs);
+ // apply K-shift if needed
+ if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
+ ggml_backend_sched_reset(sched);
- int32_t min = size - 1;
- int32_t max = 0;
+ auto * gf = lctx.graph_init();
- // everything should fit if all seq_ids are smaller than the max
- for (uint32_t s = 0; s < n_seqs; ++s) {
- const uint32_t n_seq_id = ubatch.n_seq_id[s];
- for (uint32_t j = 0; j < n_seq_id; ++j) {
- const llama_seq_id seq_id = ubatch.seq_id[s][j];
+ auto res = build_graph_shift(lctx.get_cparams(), lctx.get_ctx_compute(), gf);
- 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);
- return false;
- }
- if (j > 0) {
- llama_kv_cell & seq = cells[seq_id];
- if (seq.tail >= 0) {
- llama_kv_cell & cell = cells[seq.tail];
- // clear cells from seq_ids that become shared
- // (should not normally happen, but let's handle it anyway)
- cell.seq_id.erase(seq_id);
- seq.tail = -1;
- if (cell.seq_id.empty()) {
- cell.pos = -1;
- cell.src = -1;
- used -= 1;
- }
- }
- }
- }
+ ggml_backend_sched_alloc_graph(sched, gf);
+
+ res->set_inputs(nullptr);
+
+ lctx.graph_compute(gf, false);
+
+ need_reserve = true;
}
-#ifndef NDEBUG
{
- std::vector<int32_t> tails_verif;
- tails_verif.assign(size, -1);
- for (uint32_t i = 0; i < size; ++i) {
- llama_kv_cell & cell = cells[i];
- for (llama_seq_id seq_id : cell.seq_id) {
- if (tails_verif[seq_id] != -1) {
- LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]);
- }
- tails_verif[seq_id] = i;
- }
- }
+ has_shift = false;
+
for (uint32_t i = 0; i < size; ++i) {
- if (tails_verif[i] != cells[i].tail) {
- LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]);
- }
+ cells[i].delta = 0;
}
}
-#endif
+ }
- // find next empty cell
- uint32_t next_empty_cell = head;
+ if (do_defrag) {
+ LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
- for (uint32_t i = 0; i < size; ++i) {
- if (next_empty_cell >= size) { next_empty_cell -= size; }
- llama_kv_cell & cell = cells[next_empty_cell];
- if (cell.is_empty()) { break; }
- next_empty_cell += 1;
- }
+ if (defrag_prepare(lctx.graph_max_nodes())) {
+ ggml_backend_sched_reset(sched);
- // find usable cell range
- for (uint32_t s = 0; s < n_seqs; ++s) {
- const llama_seq_id seq_id = ubatch.seq_id[s][0];
- llama_kv_cell & seq_meta = cells[seq_id];
- bool has_cell = false;
- if (seq_meta.tail >= 0) {
- llama_kv_cell & cell = cells[seq_meta.tail];
- GGML_ASSERT(cell.has_seq_id(seq_id));
- // does this seq_id "own" the cell?
- if (cell.seq_id.size() == 1) { has_cell = true; }
- }
- if (!has_cell) {
- llama_kv_cell & empty_cell = cells[next_empty_cell];
- GGML_ASSERT(empty_cell.is_empty());
- // copy old tail into the empty cell
- if (seq_meta.tail >= 0) {
- llama_kv_cell & orig_cell = cells[seq_meta.tail];
- empty_cell.pos = orig_cell.pos;
- empty_cell.src = orig_cell.src;
- orig_cell.seq_id.erase(seq_id);
- empty_cell.seq_id.insert(seq_id); // will be overwritten
- }
- seq_meta.tail = next_empty_cell;
- // find next empty cell
- if (s + 1 < n_seqs) {
- next_empty_cell += 1;
- for (uint32_t i = 0; i < size; ++i) {
- if (next_empty_cell >= size) { next_empty_cell -= size; }
- llama_kv_cell & cell = cells[next_empty_cell];
- if (cell.is_empty()) { break; }
- next_empty_cell += 1;
- }
- }
- }
- if (min > seq_meta.tail) { min = seq_meta.tail; }
- if (max < seq_meta.tail) { max = seq_meta.tail; }
- }
+ auto * gf = lctx.graph_init();
- // gather and re-order
- for (uint32_t s = 0; s < n_seqs; ++s) {
- int32_t dst_id = s + min;
- int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
- if (dst_id != src_id) {
- llama_kv_cell & dst_cell = cells[dst_id];
- llama_kv_cell & src_cell = cells[src_id];
+ auto res = build_graph_defrag(lctx.get_cparams(), lctx.get_ctx_compute(), gf);
- std::swap(dst_cell.pos, src_cell.pos);
- std::swap(dst_cell.src, src_cell.src);
- std::swap(dst_cell.seq_id, src_cell.seq_id);
+ ggml_backend_sched_alloc_graph(sched, gf);
- // swap tails (assuming they NEVER overlap)
- for (const llama_seq_id seq_id : src_cell.seq_id) {
- cells[seq_id].tail = src_id;
- }
- for (const llama_seq_id seq_id : dst_cell.seq_id) {
- cells[seq_id].tail = dst_id;
- }
- }
- }
+ res->set_inputs(nullptr);
- // update the pos of the used seqs
- for (uint32_t s = 0; s < n_seqs; ++s) {
- const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
- int32_t cell_id = s + min;
- llama_kv_cell & cell = cells[cell_id];
+ lctx.graph_compute(gf, false);
- if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
- // What should happen when the pos backtracks or skips a value?
- // Clearing the state mid-batch would require special-casing which isn't done.
- LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
- __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
- }
- cell.pos = last_pos;
- cell.seq_id.clear();
- for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
- const llama_seq_id seq_id = ubatch.seq_id[s][j];
- cell.seq_id.insert(seq_id);
- cells[seq_id].tail = cell_id;
- }
+ need_reserve = true;
}
- // allow getting the range of used cells, from head to head + n
- head = min;
- n = max - min + 1;
- used = std::count_if(cells.begin(), cells.end(),
- [](const llama_kv_cell& cell){ return !cell.is_empty(); });
+ do_defrag = false;
+ }
+
+ return need_reserve;
+}
+
+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;
+
+ // queue defragmentation for next llama_kv_cache_update
+ if (fragmentation > thold) {
+ LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation);
+
+ do_defrag = true;
+ }
+}
+
+void llama_kv_cache_unified::set_full() {
+ n = size;
+}
+
+llama_sbatch llama_kv_cache_unified::sbatch_init(
+ const llama_batch & batch,
+ bool logits_all) {
+ return llama_sbatch(batch, hparams.n_embd, true, logits_all);
+}
+
+llama_ubatch llama_kv_cache_unified::ubatch_next(
+ llama_sbatch & sbatch,
+ uint32_t n_ubatch,
+ bool embd_pooled) const {
+ GGML_UNUSED(embd_pooled);
+ return sbatch.split_simple(n_ubatch);
+}
+
+bool llama_kv_cache_unified::find_slot(
+ const llama_ubatch & ubatch) {
+ const uint32_t n_tokens = ubatch.n_tokens;
+ const uint32_t n_seqs = ubatch.n_seqs;
+ const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
- // sanity check
- return n >= n_seqs;
+ // if we have enough unused cells before the current head ->
+ // better to start searching from the beginning of the cache, hoping to fill it
+ if (head > used + 2*ubatch.n_tokens) {
+ head = 0;
}
// otherwise, one cell per token.
pending.ranges.push_back({head, head + n_tokens});
+ // 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)));
+
+ //printf("n = %5d, used = %5d, head = %5d\n", n, used, head);
+
return true;
}
-uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) const {
- // the FA kernels require padding to avoid extra runtime boundary checks
- return cparams.flash_attn ? 256u : 32u;
+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;
}
-uint32_t llama_kv_cache_unified::cell_max() const {
- for (uint32_t i = size; i > 0; --i) {
- const llama_kv_cell & cell = cells[i - 1];
+int32_t llama_kv_cache_unified::get_used_cells() const {
+ return used;
+}
- if (cell.pos >= 0 && !cell.is_empty()) {
- return i;
- }
+bool llama_kv_cache_unified::get_can_shift() const {
+ return can_shift;
+}
+
+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 0;
+ return pos_max;
+}
+
+size_t llama_kv_cache_unified::total_size() const {
+ size_t size = 0;
+ for (const auto & buf : bufs) {
+ size += ggml_backend_buffer_get_size(buf.get());
+ }
+
+ return size;
}
size_t llama_kv_cache_unified::size_k_bytes() const {
return size_v_bytes;
}
-bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
- const uint32_t n_layer = hparams.n_layer;
+ggml_tensor * llama_kv_cache_unified::build_rope_shift(
+ const llama_cparams & cparams,
+ ggml_context * ctx,
+ ggml_tensor * cur,
+ ggml_tensor * shift,
+ ggml_tensor * factors,
+ float freq_base,
+ float freq_scale) const {
+ const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;
- const uint32_t n_kv = cell_max();
- const uint32_t n_used = used;
+ const auto & yarn_ext_factor = cparams.yarn_ext_factor;
+ const auto & yarn_beta_fast = cparams.yarn_beta_fast;
+ const auto & yarn_beta_slow = cparams.yarn_beta_slow;
- assert(n_used <= n_kv);
+ const auto & n_rot = hparams.n_rot;
+ const auto & rope_type = hparams.rope_type;
- //const int64_t t_start = ggml_time_us();
+ // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly.
+ // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
+ const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor;
- // number of cells moved
- uint32_t n_moves = 0;
+ ggml_tensor * tmp;
- // each move requires 6*n_layer tensors (see graph_build_kv_self_defrag)
- // - source view, destination view, copy operation
- // - x2 for keys and values
- //const uint32_t max_moves = max_nodes()/(6*n_layer);
- // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
- const uint32_t max_moves = (n_max_nodes - 2*n_layer)/(6*n_layer);
+ if (ggml_is_quantized(cur->type)) {
+ // dequantize to f32 -> RoPE -> quantize back
+ tmp = ggml_cast(ctx, cur, GGML_TYPE_F32);
- // determine which KV cells to move where
- //
- // cell i moves to ids[i]
- //
- // if ids[i] == i || ids[i] == n_kv, then cell i is not moved
- //
- auto & ids = defrag_info.ids;
+ tmp = ggml_rope_ext(ctx, tmp,
+ shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow);
- ids.clear();
- ids.resize(n_kv, n_kv);
+ tmp = ggml_cpy(ctx, tmp, cur);
+ } else {
+ // we rotate only the first n_rot dimensions
+ tmp = ggml_rope_ext_inplace(ctx, cur,
+ shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow);
+ }
- for (uint32_t i0 = 0; i0 < n_used; ++i0) {
- const auto & cell0 = cells[i0];
+ return tmp;
+}
- if (!cell0.is_empty()) {
- ids[i0] = i0;
+class llm_graph_input_k_shift : public llm_graph_input_i {
+public:
+ llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
+ virtual ~llm_graph_input_k_shift() = default;
- continue;
- }
+ void set_input(const llama_ubatch * ubatch) override;
- // found a hole - fill it with data from the end of the cache
+ ggml_tensor * k_shift; // I32 [kv_size]
- uint32_t nh = 1;
+ const llama_kv_cache_unified * kv_self;
+};
- // determine the size of the hole
- while (i0 + nh < n_used && cells[i0 + nh].is_empty()) {
- nh++;
+void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) {
+ GGML_UNUSED(ubatch);
+
+ if (k_shift) {
+ assert(ggml_backend_buffer_is_host(k_shift->buffer));
+
+ int32_t * data = (int32_t *) k_shift->data;
+
+ for (uint32_t i = 0; i < kv_self->size; ++i) {
+ data[i] = kv_self->cells[i].delta;
}
+ }
+}
- uint32_t nf = 0;
- uint32_t is = n_kv - 1;
+llm_graph_result_ptr llama_kv_cache_unified::build_graph_shift(
+ const llama_cparams & cparams,
+ ggml_context * ctx,
+ ggml_cgraph * gf) const {
+ auto res = std::make_unique<llm_graph_result>();
- // starting from the end, find nh non-empty cells
- for (; is > i0; --is) {
- const auto & cell1 = cells[is];
+ const auto & n_layer = hparams.n_layer;
- if (cell1.is_empty() || ids[is] != n_kv) {
- continue;
- }
+ const auto & n_embd_head_k = hparams.n_embd_head_k;
+ //const auto & n_embd_head_v = hparams.n_embd_head_v;
- // non-empty cell which is not yet moved
- nf++;
+ const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max;
+
+ //GGML_ASSERT(kv_self->size == n_ctx);
+
+ auto inp = std::make_unique<llm_graph_input_k_shift>(this);
+
+ inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, cparams.n_ctx);
+ ggml_set_input(inp->k_shift);
+
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const int64_t n_head_kv = hparams.n_head_kv(il);
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+
+ const bool is_swa = hparams.is_swa(il);
+
+ // note: the swa rope params could become part of the cparams in the future
+ // if we decide to make them configurable, like the non-sliding ones
+ const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
+ const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale;
+
+ ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il);
+
+ ggml_tensor * k =
+ ggml_view_3d(ctx, k_l[il],
+ n_embd_head_k, n_head_kv, size,
+ ggml_row_size(k_l[il]->type, n_embd_head_k),
+ ggml_row_size(k_l[il]->type, n_embd_k_gqa),
+ 0);
+
+ ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l);
+
+ ggml_build_forward_expand(gf, cur);
+ }
+
+ res->add_input(std::move(inp));
+
+ return res;
+}
+
+llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
+ const llama_cparams & cparams,
+ ggml_context * ctx,
+ ggml_cgraph * gf) const {
+ auto res = std::make_unique<llm_graph_result>();
+
+ const auto & ids = defrag_info.ids;
+
+#if 0
+ // CPU defrag
+ //
+ // TODO: optimizations are possible:
+ // - multiple threads
+ // - avoid copying to the host memory when already there
+ //
+ // likely not worth the effort, as we have ggml_graph based defrag
+ //
+
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+
+ const uint32_t kv_size = size;
+
+ std::vector<uint8_t> buf_k;
+ std::vector<uint8_t> buf_v;
+
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
+ const size_t k_size = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size);
+
+ const size_t v_size_el = ggml_type_size(v_l[il]->type);
+ const size_t v_size = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size);
+
+ buf_k.resize(k_size);
+ buf_v.resize(v_size);
+
+ ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size());
+ ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size());
+
+ // batch move [i, i+nm) to [id, id+nm)
+ // note: cells can move only to a lower index
+ for (uint32_t i = 0; i < n_kv; ++i) {
+ const uint32_t id = ids[i];
+
+ if (i == id || id == n_kv) {
+ continue;
+ }
+
+ uint32_t nm = 1;
+
+ while (i + nm < n_kv && ids[i + nm] == id + nm) {
+ nm++;
+ }
+
+ // move keys
+ {
+ const int64_t os = i*k_size_row;
+ const int64_t od = id*k_size_row;
+
+ memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
+ }
+
+ // move values (note: they are transposed)
+ {
+ const int64_t os = i;
+ const int64_t od = id;
+
+ for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+ memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
+ }
+ }
+
+ i += nm - 1;
+ }
+
+ ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size());
+ ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size());
+ }
+#else
+ for (uint32_t i = 0; i < ids.size(); ++i) {
+ const uint32_t id = ids[i];
+
+ if (i == id || id == ids.size()) {
+ continue;
+ }
+
+ uint32_t nm = 1;
+
+ while (i + nm < ids.size() && ids[i + nm] == id + nm) {
+ nm++;
+ }
+
+ for (uint32_t il = 0; il < hparams.n_layer; ++il) { // NOLINT
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+ const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
+ ggml_tensor * view_k_src = ggml_view_2d(ctx, k_l[il],
+ n_embd_k_gqa, nm,
+ ggml_row_size(k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(k_l[il]->type, n_embd_k_gqa*i));
+
+ ggml_tensor * view_k_dst = ggml_view_2d(ctx, k_l[il],
+ n_embd_k_gqa, nm,
+ ggml_row_size(k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(k_l[il]->type, n_embd_k_gqa*id));
+
+ ggml_tensor * view_v_src;
+ ggml_tensor * view_v_dst;
+
+ if (cparams.flash_attn) {
+ // NOTE: the V cache is not transposed when using flash attention
+ view_v_src = ggml_view_2d(ctx, v_l[il],
+ n_embd_v_gqa, nm,
+ ggml_row_size(v_l[il]->type, n_embd_v_gqa),
+ ggml_row_size(v_l[il]->type, n_embd_v_gqa*i));
+
+ view_v_dst = ggml_view_2d(ctx, v_l[il],
+ n_embd_v_gqa, nm,
+ ggml_row_size(v_l[il]->type, n_embd_v_gqa),
+ ggml_row_size(v_l[il]->type, n_embd_v_gqa*id));
+ } else {
+ view_v_src = ggml_view_2d(ctx, v_l[il],
+ nm, n_embd_v_gqa,
+ ggml_row_size(v_l[il]->type, size),
+ ggml_row_size(v_l[il]->type, i));
+
+ view_v_dst = ggml_view_2d(ctx, v_l[il],
+ nm, n_embd_v_gqa,
+ ggml_row_size(v_l[il]->type, size),
+ ggml_row_size(v_l[il]->type, id));
+ }
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx, view_k_src, view_k_dst));
+ ggml_build_forward_expand(gf, ggml_cpy(ctx, view_v_src, view_v_dst));
+ }
+
+ i += nm - 1;
+ }
+
+ //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
+#endif
+
+ return res;
+}
+
+bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
+ const uint32_t n_layer = hparams.n_layer;
+
+ const uint32_t n_kv = cell_max();
+ const uint32_t n_used = used;
+
+ assert(n_used <= n_kv);
+
+ //const int64_t t_start = ggml_time_us();
+
+ // number of cells moved
+ uint32_t n_moves = 0;
+
+ // each move requires 6*n_layer tensors (see graph_build_kv_self_defrag)
+ // - source view, destination view, copy operation
+ // - x2 for keys and values
+ //const uint32_t max_moves = max_nodes()/(6*n_layer);
+ // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
+ const uint32_t max_moves = (n_max_nodes - 2*n_layer)/(6*n_layer);
+
+ // determine which KV cells to move where
+ //
+ // cell i moves to ids[i]
+ //
+ // if ids[i] == i || ids[i] == n_kv, then cell i is not moved
+ //
+ auto & ids = defrag_info.ids;
+
+ ids.clear();
+ ids.resize(n_kv, n_kv);
+
+ for (uint32_t i0 = 0; i0 < n_used; ++i0) {
+ const auto & cell0 = cells[i0];
+
+ if (!cell0.is_empty()) {
+ ids[i0] = i0;
+
+ continue;
+ }
+
+ // found a hole - fill it with data from the end of the cache
+
+ uint32_t nh = 1;
+
+ // determine the size of the hole
+ while (i0 + nh < n_used && cells[i0 + nh].is_empty()) {
+ nh++;
+ }
+
+ uint32_t nf = 0;
+ uint32_t is = n_kv - 1;
+
+ // starting from the end, find nh non-empty cells
+ for (; is > i0; --is) {
+ const auto & cell1 = cells[is];
+
+ if (cell1.is_empty() || ids[is] != n_kv) {
+ continue;
+ }
+
+ // non-empty cell which is not yet moved
+ nf++;
if (nf == nh) {
break;
cells[i0 + nf] = cell1;
// clear the old cell and move the head there
- cell1 = llama_kv_cell();
+ cell1 = kv_cell();
head = n_used;
if (!cont) {
return false;
}
- LLAMA_LOG_DEBUG("(tmp log) KV defrag cell moves: %u\n", n_moves);
+ LLAMA_LOG_DEBUG("%s: (tmp log) KV defrag cell moves: %u\n", __func__, n_moves);
- LLAMA_LOG_DEBUG("expected gf nodes: %u\n", 6*n_moves*n_layer);
+ LLAMA_LOG_DEBUG("%s: expected gf nodes: %u\n", __func__, 6*n_moves*n_layer);
return true;
}
+uint32_t llama_kv_cache_unified::cell_max() const {
+ for (uint32_t i = size; i > 0; --i) {
+ const kv_cell & cell = cells[i - 1];
+
+ if (cell.pos >= 0 && !cell.is_empty()) {
+ return i;
+ }
+ }
+
+ return 0;
+}
+
void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
uint32_t cell_count = 0;
clear();
for (uint32_t i = 0; i < cell_count; ++i) {
- llama_kv_cell & cell = cells[i];
+ kv_cell & cell = cells[i];
llama_pos pos;
uint32_t n_seq_id;
}
cell.seq_id.insert(seq_id);
-
- if (recurrent) {
- int32_t & tail = cells[seq_id].tail;
- if (tail != -1) {
- LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail);
- return false;
- }
- tail = i;
- }
}
}
used = cell_count;
}
- if (recurrent) {
- for (uint32_t i = 0; i < cell_count; ++i) {
- uint32_t cell_id = head + i;
- // make sure the recurrent states will keep their restored state
- cells[cell_id].src = cell_id;
- }
- }
-
return true;
}
LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size);
return false;
}
- if (v_trans != (bool) v_trans) {
+ if (this->v_trans != (bool) v_trans) {
+ LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__);
+ return false;
+ }
+
+ // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+
+ // Read type of key
+ int32_t k_type_i_ref;
+ io.read_to(&k_type_i_ref, sizeof(k_type_i_ref));
+ const int32_t k_type_i = (int32_t) k_l[il]->type;
+ if (k_type_i != k_type_i_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
+ return false;
+ }
+
+ // Read row size of key
+ uint64_t k_size_row_ref;
+ io.read_to(&k_size_row_ref, sizeof(k_size_row_ref));
+ const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
+ if (k_size_row != k_size_row_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il);
+ return false;
+ }
+
+ if (cell_count) {
+ // Read and set the keys for the whole cell range
+ ggml_backend_tensor_set(k_l[il], io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
+ }
+ }
+
+ if (!this->v_trans) {
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Read type of value
+ int32_t v_type_i_ref;
+ io.read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+ const int32_t v_type_i = (int32_t)v_l[il]->type;
+ if (v_type_i != v_type_i_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+ return false;
+ }
+
+ // Read row size of value
+ uint64_t v_size_row_ref;
+ io.read_to(&v_size_row_ref, sizeof(v_size_row_ref));
+ const size_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa);
+ if (v_size_row != v_size_row_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il);
+ return false;
+ }
+
+ if (cell_count) {
+ // Read and set the values for the whole cell range
+ ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row);
+ }
+ }
+ } else {
+ // For each layer, read the values for each cell (transposed)
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Read type of value
+ int32_t v_type_i_ref;
+ io.read_to(&v_type_i_ref, sizeof(v_type_i_ref));
+ const int32_t v_type_i = (int32_t)v_l[il]->type;
+ if (v_type_i != v_type_i_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+ return false;
+ }
+
+ // Read element size of value
+ uint32_t v_size_el_ref;
+ io.read_to(&v_size_el_ref, sizeof(v_size_el_ref));
+ const size_t v_size_el = ggml_type_size(v_l[il]->type);
+ if (v_size_el != v_size_el_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il);
+ return false;
+ }
+
+ // Read GQA embedding size
+ uint32_t n_embd_v_gqa_ref;
+ io.read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
+ if (n_embd_v_gqa != n_embd_v_gqa_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il);
+ return false;
+ }
+
+ if (cell_count) {
+ // For each row in the transposed matrix, read the values for the whole cell range
+ for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+ const size_t dst_offset = (head + j * size) * v_size_el;
+ ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+ }
+ }
+ }
+ }
+
+ return true;
+}
+
+//
+// llama_kv_cache_recurrent
+//
+
+llama_kv_cache_recurrent::llama_kv_cache_recurrent(
+ const llama_model & model,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool offload,
+ uint32_t kv_size) : hparams(model.hparams) {
+ 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);
+
+ head = 0;
+ size = kv_size;
+ used = 0;
+
+ this->type_k = type_k;
+ this->type_v = type_v;
+
+ cells.clear();
+ cells.resize(kv_size);
+
+ // create a context for each buffer type
+ std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+ auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+ auto it = ctx_map.find(buft);
+ if (it == ctx_map.end()) {
+ ggml_init_params params = {
+ /*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()),
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ true,
+ };
+
+ ggml_context * ctx = ggml_init(params);
+ if (!ctx) {
+ return nullptr;
+ }
+
+ ctx_map[buft] = ctx;
+ ctxs.emplace_back(ctx);
+
+ return ctx;
+ }
+
+ return it->second;
+ };
+
+ k_l.reserve(n_layer);
+ v_l.reserve(n_layer);
+
+ for (int i = 0; i < n_layer; i++) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
+
+ const char * dev_name = "CPU";
+
+ ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type();
+
+ if (offload) {
+ auto * dev = model.dev_layer(i);
+ buft = ggml_backend_dev_buffer_type(dev);
+
+ dev_name = ggml_backend_dev_name(dev);
+ }
+
+ LLAMA_LOG_DEBUG("%s, layer %3d: dev = %s\n", __func__, i, dev_name);
+
+ ggml_context * ctx = ctx_for_buft(buft);
+ if (!ctx) {
+ throw std::runtime_error("failed to create ggml context for kv cache");
+ }
+
+ ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+ ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
+ ggml_format_name(k, "cache_k_l%d", i);
+ ggml_format_name(v, "cache_v_l%d", i);
+ k_l.push_back(k);
+ v_l.push_back(v);
+ }
+
+ // allocate tensors and initialize the buffers to avoid NaNs in the padding
+ for (auto it : ctx_map) {
+ auto * buft = it.first;
+ auto * ctx = it.second;
+
+ ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+ if (!buf) {
+ throw std::runtime_error("failed to allocate buffer for kv cache");
+ }
+ ggml_backend_buffer_clear(buf, 0);
+ LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+ bufs.emplace_back(buf);
+ }
+
+ {
+ const size_t memory_size_k = size_k_bytes();
+ const size_t memory_size_v = size_v_bytes();
+
+ LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
+ (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
+ 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));
+ }
+}
+
+void llama_kv_cache_recurrent::clear() {
+ for (int32_t i = 0; i < (int32_t) size; ++i) {
+ cells[i].pos = -1;
+ cells[i].seq_id.clear();
+ cells[i].src = -1;
+ cells[i].tail = -1;
+ }
+ head = 0;
+ used = 0;
+
+ for (auto & buf : bufs) {
+ ggml_backend_buffer_clear(buf.get(), 0);
+ }
+}
+
+bool llama_kv_cache_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+ uint32_t new_head = size;
+
+ if (p0 < 0) {
+ p0 = 0;
+ }
+
+ if (p1 < 0) {
+ p1 = std::numeric_limits<llama_pos>::max();
+ }
+
+ // models like Mamba or RWKV can't have a state partially erased
+ if (seq_id >= (int64_t) size) {
+ // could be fatal
+ return false;
+ }
+ if (0 <= seq_id) {
+ int32_t & tail_id = cells[seq_id].tail;
+ if (tail_id >= 0) {
+ const kv_cell & cell = cells[tail_id];
+ // partial intersection is invalid
+ if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
+ return false;
+ }
+ // invalidate tails which will be cleared
+ if (p0 <= cell.pos && cell.pos < p1) {
+ tail_id = -1;
+ }
+ }
+ } else {
+ // seq_id is negative, then the range should include everything or nothing
+ if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
+ return false;
+ }
+ }
+
+ for (uint32_t i = 0; i < size; ++i) {
+ if (cells[i].pos >= p0 && cells[i].pos < p1) {
+ if (seq_id < 0) {
+ cells[i].seq_id.clear();
+ } else if (cells[i].has_seq_id(seq_id)) {
+ cells[i].seq_id.erase(seq_id);
+ } else {
+ continue;
+ }
+ if (cells[i].is_empty()) {
+ // keep count of the number of used cells
+ if (cells[i].pos >= 0) {
+ used--;
+ }
+ cells[i].pos = -1;
+ cells[i].src = -1;
+ if (new_head == size) {
+ new_head = i;
+ }
+ }
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ if (new_head != size && new_head < head) {
+ head = new_head;
+ }
+
+ return true;
+}
+
+void llama_kv_cache_recurrent::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+ if (seq_id_src == seq_id_dst) {
+ return;
+ }
+
+ if (p0 < 0) {
+ p0 = 0;
+ }
+
+ if (p1 < 0) {
+ p1 = std::numeric_limits<llama_pos>::max();
+ }
+
+ if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) {
+ kv_cell & tail_src = cells[seq_id_src];
+ kv_cell & tail_dst = cells[seq_id_dst];
+ if (tail_dst.tail >= 0) {
+ // clear destination seq_id if it wasn't empty
+ kv_cell & cell_dst = cells[tail_dst.tail];
+
+ cell_dst.seq_id.erase(seq_id_dst);
+ tail_dst.tail = -1;
+ if (cell_dst.seq_id.empty()) {
+ cell_dst.pos = -1;
+ cell_dst.src = -1;
+ used -= 1;
+ }
+ }
+ if (tail_src.tail >= 0) {
+ kv_cell & cell_src = cells[tail_src.tail];
+
+ cell_src.seq_id.insert(seq_id_dst);
+ tail_dst.tail = tail_src.tail;
+ }
+ }
+}
+
+void llama_kv_cache_recurrent::seq_keep(llama_seq_id seq_id) {
+ uint32_t new_head = size;
+
+ for (uint32_t i = 0; i < size; ++i) {
+ if ((llama_seq_id) i != seq_id) {
+ cells[i].tail = -1;
+ }
+
+ if (!cells[i].has_seq_id(seq_id)) {
+ if (cells[i].pos >= 0) {
+ used--;
+ }
+
+ cells[i].pos = -1;
+ cells[i].src = -1;
+ cells[i].seq_id.clear();
+
+ if (new_head == size){
+ new_head = i;
+ }
+ } else {
+ cells[i].seq_id.clear();
+ cells[i].seq_id.insert(seq_id);
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ if (new_head != size && new_head < head) {
+ head = new_head;
+ }
+}
+
+void llama_kv_cache_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+ if (delta == 0) {
+ return;
+ }
+
+ if (p0 < 0) {
+ p0 = 0;
+ }
+
+ if (p1 < 0) {
+ p1 = std::numeric_limits<llama_pos>::max();
+ }
+
+ // If there is no range then return early to avoid looping over the
+ if (p0 == p1) {
+ return;
+ }
+
+ // for Mamba-like or RWKV models, only the pos needs to be shifted
+ if (0 <= seq_id && seq_id < (int64_t) size) {
+ const int32_t tail_id = cells[seq_id].tail;
+ if (tail_id >= 0) {
+ kv_cell & cell = cells[tail_id];
+ if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+ cell.pos += delta;
+ }
+ }
+ }
+}
+
+void llama_kv_cache_recurrent::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+ if (d == 1) {
+ return;
+ }
+
+ if (p0 < 0) {
+ p0 = 0;
+ }
+
+ if (p1 < 0) {
+ p1 = std::numeric_limits<llama_pos>::max();
+ }
+
+ // If there is no range then return early to avoid looping over the cache.
+ if (p0 == p1) {
+ return;
+ }
+
+ // for Mamba-like or RWKV models, only the pos needs to be changed
+ if (0 <= seq_id && seq_id < (int64_t) size) {
+ const int32_t tail_id = cells[seq_id].tail;
+ if (tail_id >= 0) {
+ kv_cell & cell = cells[tail_id];
+ if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+ cell.pos /= d;
+ }
+ }
+ }
+}
+
+llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const {
+ llama_pos result = 0;
+
+ for (uint32_t i = 0; i < size; ++i) {
+ if (cells[i].has_seq_id(seq_id)) {
+ result = std::max(result, cells[i].pos);
+ }
+ }
+
+ return result;
+}
+
+void llama_kv_cache_recurrent::restore() {
+ if (pending.ranges.empty()) {
+ return;
+ }
+
+ seq_rm(-1, -1, -1);
+}
+
+void llama_kv_cache_recurrent::commit() {
+ pending.ranges.clear();
+}
+
+bool llama_kv_cache_recurrent::update(llama_context & lctx) {
+ GGML_UNUSED(lctx);
+ return false;
+}
+
+void llama_kv_cache_recurrent::defrag_sched(float thold) {
+ GGML_UNUSED(thold);
+ // noop
+}
+
+void llama_kv_cache_recurrent::set_full() {
+ n = size;
+}
+
+llama_sbatch llama_kv_cache_recurrent::sbatch_init(
+ const llama_batch & batch,
+ bool logits_all) {
+ return llama_sbatch(batch, hparams.n_embd, false, logits_all);
+}
+
+llama_ubatch llama_kv_cache_recurrent::ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const {
+ if (embd_pooled) {
+ // Pooled embeddings cannot be split across ubatches (yet)
+ return sbatch.split_seq(n_ubatch);
+ }
+
+ return sbatch.split_equal(n_ubatch);
+}
+
+bool llama_kv_cache_recurrent::find_slot(
+ const llama_ubatch & ubatch) {
+ const uint32_t n_tokens = ubatch.n_tokens;
+ const uint32_t n_seqs = ubatch.n_seqs;
+
+ const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
+
+ // if we have enough unused cells before the current head ->
+ // better to start searching from the beginning of the cache, hoping to fill it
+ if (head > used + 2*n_tokens) {
+ head = 0;
+ }
+
+ // For recurrent state architectures (like Mamba or RWKV),
+ // each cache cell can store the state for a whole sequence.
+ // A slot should be always be contiguous.
+
+ // can only process batches with an equal number of new tokens in each sequence
+ GGML_ASSERT(ubatch.equal_seqs);
+
+ int32_t min = size - 1;
+ int32_t max = 0;
+
+ // everything should fit if all seq_ids are smaller than the max
+ for (uint32_t s = 0; s < n_seqs; ++s) {
+ const uint32_t n_seq_id = ubatch.n_seq_id[s];
+ for (uint32_t j = 0; j < n_seq_id; ++j) {
+ const llama_seq_id seq_id = ubatch.seq_id[s][j];
+
+ 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);
+ return false;
+ }
+ if (j > 0) {
+ kv_cell & seq = cells[seq_id];
+ if (seq.tail >= 0) {
+ kv_cell & cell = cells[seq.tail];
+ // clear cells from seq_ids that become shared
+ // (should not normally happen, but let's handle it anyway)
+ cell.seq_id.erase(seq_id);
+ seq.tail = -1;
+ if (cell.seq_id.empty()) {
+ cell.pos = -1;
+ cell.src = -1;
+ used -= 1;
+ }
+ }
+ }
+ }
+ }
+
+#ifndef NDEBUG
+ {
+ std::vector<int32_t> tails_verif;
+ tails_verif.assign(size, -1);
+ for (uint32_t i = 0; i < size; ++i) {
+ kv_cell & cell = cells[i];
+ for (llama_seq_id seq_id : cell.seq_id) {
+ if (tails_verif[seq_id] != -1) {
+ LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]);
+ }
+ tails_verif[seq_id] = i;
+ }
+ }
+ for (uint32_t i = 0; i < size; ++i) {
+ if (tails_verif[i] != cells[i].tail) {
+ LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]);
+ }
+ }
+ }
+#endif
+
+ // find next empty cell
+ uint32_t next_empty_cell = head;
+
+ for (uint32_t i = 0; i < size; ++i) {
+ if (next_empty_cell >= size) { next_empty_cell -= size; }
+ kv_cell & cell = cells[next_empty_cell];
+ if (cell.is_empty()) { break; }
+ next_empty_cell += 1;
+ }
+
+ // find usable cell range
+ for (uint32_t s = 0; s < n_seqs; ++s) {
+ const llama_seq_id seq_id = ubatch.seq_id[s][0];
+ kv_cell & seq_meta = cells[seq_id];
+ bool has_cell = false;
+ if (seq_meta.tail >= 0) {
+ kv_cell & cell = cells[seq_meta.tail];
+ GGML_ASSERT(cell.has_seq_id(seq_id));
+ // does this seq_id "own" the cell?
+ if (cell.seq_id.size() == 1) { has_cell = true; }
+ }
+ if (!has_cell) {
+ kv_cell & empty_cell = cells[next_empty_cell];
+ GGML_ASSERT(empty_cell.is_empty());
+ // copy old tail into the empty cell
+ if (seq_meta.tail >= 0) {
+ kv_cell & orig_cell = cells[seq_meta.tail];
+ empty_cell.pos = orig_cell.pos;
+ empty_cell.src = orig_cell.src;
+ orig_cell.seq_id.erase(seq_id);
+ empty_cell.seq_id.insert(seq_id); // will be overwritten
+ }
+ seq_meta.tail = next_empty_cell;
+ // find next empty cell
+ if (s + 1 < n_seqs) {
+ next_empty_cell += 1;
+ for (uint32_t i = 0; i < size; ++i) {
+ if (next_empty_cell >= size) { next_empty_cell -= size; }
+ kv_cell & cell = cells[next_empty_cell];
+ if (cell.is_empty()) { break; }
+ next_empty_cell += 1;
+ }
+ }
+ }
+ if (min > seq_meta.tail) { min = seq_meta.tail; }
+ if (max < seq_meta.tail) { max = seq_meta.tail; }
+ }
+
+ // gather and re-order
+ for (uint32_t s = 0; s < n_seqs; ++s) {
+ int32_t dst_id = s + min;
+ int32_t src_id = cells[ubatch.seq_id[s][0]].tail;
+ if (dst_id != src_id) {
+ kv_cell & dst_cell = cells[dst_id];
+ kv_cell & src_cell = cells[src_id];
+
+ std::swap(dst_cell.pos, src_cell.pos);
+ std::swap(dst_cell.src, src_cell.src);
+ std::swap(dst_cell.seq_id, src_cell.seq_id);
+
+ // swap tails (assuming they NEVER overlap)
+ for (const llama_seq_id seq_id : src_cell.seq_id) {
+ cells[seq_id].tail = src_id;
+ }
+ for (const llama_seq_id seq_id : dst_cell.seq_id) {
+ cells[seq_id].tail = dst_id;
+ }
+ }
+ }
+
+ // update the pos of the used seqs
+ for (uint32_t s = 0; s < n_seqs; ++s) {
+ const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
+ int32_t cell_id = s + min;
+ kv_cell & cell = cells[cell_id];
+
+ if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
+ // What should happen when the pos backtracks or skips a value?
+ // Clearing the state mid-batch would require special-casing which isn't done.
+ LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
+ __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
+ }
+ cell.pos = last_pos;
+ cell.seq_id.clear();
+ for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
+ const llama_seq_id seq_id = ubatch.seq_id[s][j];
+ cell.seq_id.insert(seq_id);
+ cells[seq_id].tail = cell_id;
+ }
+ }
+
+ // allow getting the range of used cells, from head to head + n
+ head = min;
+ n = max - min + 1;
+ used = std::count_if(cells.begin(), cells.end(),
+ [](const kv_cell & cell){ return !cell.is_empty(); });
+
+ // sanity check
+ 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;
+}
+
+int32_t llama_kv_cache_recurrent::s_copy(int i) const {
+ const uint32_t cell_id = i + head;
+
+ //////////////////////////////////////////////
+ // TODO: this should not mutate the KV cache !
+ kv_cell & cell = const_cast<kv_cell &>(cells[cell_id]);
+
+ // prevent out-of-bound sources
+ if (cell.src < 0 || (uint32_t) cell.src >= size) {
+ cell.src = cell_id;
+ }
+
+ int32_t res = cell.src;
+
+ // TODO: do not mutate the KV cache
+ // ensure copy only happens once
+ if (cell.src != (int32_t) cell_id) {
+ cell.src = cell_id;
+ }
+
+ return res;
+}
+
+float llama_kv_cache_recurrent::s_mask(int i) const {
+ const uint32_t cell_id = i + head;
+
+ //////////////////////////////////////////////
+ // TODO: this should not mutate the KV cache !
+ kv_cell & cell = const_cast<kv_cell &>(cells[cell_id]);
+
+ float res = (float) (cell.src >= 0);
+
+ // only clear once
+ if (cell.src < 0) {
+ cell.src = cell_id;
+ }
+
+ return res;
+}
+
+uint32_t llama_kv_cache_recurrent::cell_max() const {
+ for (uint32_t i = size; i > 0; --i) {
+ const kv_cell & cell = cells[i - 1];
+
+ if (cell.pos >= 0 && !cell.is_empty()) {
+ return i;
+ }
+ }
+
+ return 0;
+}
+
+size_t llama_kv_cache_recurrent::total_size() const {
+ size_t size = 0;
+ for (const auto & buf : bufs) {
+ size += ggml_backend_buffer_get_size(buf.get());
+ }
+
+ return size;
+}
+
+size_t llama_kv_cache_recurrent::size_k_bytes() const {
+ size_t size_k_bytes = 0;
+
+ for (const auto & k : k_l) {
+ size_k_bytes += ggml_nbytes(k);
+ }
+
+ return size_k_bytes;
+}
+
+size_t llama_kv_cache_recurrent::size_v_bytes() const {
+ size_t size_v_bytes = 0;
+
+ for (const auto & v : v_l) {
+ size_v_bytes += ggml_nbytes(v);
+ }
+
+ return size_v_bytes;
+}
+
+void llama_kv_cache_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
+ std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+ uint32_t cell_count = 0;
+
+ // Count the number of cells with the specified seq_id
+ // Find all the ranges of cells with this seq id (or all, when -1)
+ uint32_t cell_range_begin = size;
+ for (uint32_t i = 0; i < size; ++i) {
+ const auto & cell = cells[i];
+ if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
+ ++cell_count;
+ if (cell_range_begin == size) {
+ cell_range_begin = i;
+ }
+ } else {
+ if (cell_range_begin != size) {
+ cell_ranges.emplace_back(cell_range_begin, i);
+ cell_range_begin = size;
+ }
+ }
+ }
+ if (cell_range_begin != size) {
+ cell_ranges.emplace_back(cell_range_begin, size);
+ }
+
+ // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
+ uint32_t cell_count_check = 0;
+ for (const auto & range : cell_ranges) {
+ cell_count_check += range.second - range.first;
+ }
+ GGML_ASSERT(cell_count == cell_count_check);
+
+ io.write(&cell_count, sizeof(cell_count));
+
+ state_write_meta(io, cell_ranges, seq_id);
+ state_write_data(io, cell_ranges);
+}
+
+void llama_kv_cache_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
+ uint32_t cell_count;
+ io.read_to(&cell_count, sizeof(cell_count));
+
+ bool res = true;
+ res = res && state_read_meta(io, cell_count, seq_id);
+ res = res && state_read_data(io, cell_count);
+
+ if (!res) {
+ if (seq_id == -1) {
+ clear();
+ } else {
+ seq_rm(seq_id, -1, -1);
+ }
+ throw std::runtime_error("failed to restore kv cache");
+ }
+}
+
+void llama_kv_cache_recurrent::state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id) const {
+ for (const auto & range : cell_ranges) {
+ for (uint32_t i = range.first; i < range.second; ++i) {
+ const auto & cell = cells[i];
+ const llama_pos pos = cell.pos;
+ const uint32_t n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
+
+ io.write(&pos, sizeof(pos));
+ io.write(&n_seq_id, sizeof(n_seq_id));
+
+ if (n_seq_id) {
+ for (auto seq_id : cell.seq_id) {
+ io.write(&seq_id, sizeof(seq_id));
+ }
+ }
+ }
+ }
+}
+
+void llama_kv_cache_recurrent::state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const {
+ const uint32_t v_trans = 0;
+ const uint32_t n_layer = hparams.n_layer;
+
+ io.write(&v_trans, sizeof(v_trans));
+ io.write(&n_layer, sizeof(n_layer));
+
+ std::vector<uint8_t> tmp_buf;
+
+ // Iterate and write all the keys first, each row is a cell
+ // Get whole range at a time
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+
+ // Write key type
+ const int32_t k_type_i = (int32_t)k_l[il]->type;
+ io.write(&k_type_i, sizeof(k_type_i));
+
+ // Write row size of key
+ const uint64_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa);
+ io.write(&k_size_row, sizeof(k_size_row));
+
+ // Read each range of cells of k_size length each into tmp_buf and write out
+ for (const auto & range : cell_ranges) {
+ const size_t range_size = range.second - range.first;
+ const size_t buf_size = range_size * k_size_row;
+ io.write_tensor(k_l[il], range.first * k_size_row, buf_size);
+ }
+ }
+
+ if (!v_trans) {
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Write value type
+ const int32_t v_type_i = (int32_t)v_l[il]->type;
+ io.write(&v_type_i, sizeof(v_type_i));
+
+ // Write row size of value
+ const uint64_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa);
+ io.write(&v_size_row, sizeof(v_size_row));
+
+ // Read each range of cells of v_size length each into tmp_buf and write out
+ for (const auto & range : cell_ranges) {
+ const size_t range_size = range.second - range.first;
+ const size_t buf_size = range_size * v_size_row;
+ io.write_tensor(v_l[il], range.first * v_size_row, buf_size);
+ }
+ }
+ } else {
+ // When v is transposed, we also need the element size and get the element ranges from each row
+ const uint32_t kv_size = size;
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Write value type
+ const int32_t v_type_i = (int32_t)v_l[il]->type;
+ io.write(&v_type_i, sizeof(v_type_i));
+
+ // Write element size
+ const uint32_t v_size_el = ggml_type_size(v_l[il]->type);
+ io.write(&v_size_el, sizeof(v_size_el));
+
+ // Write GQA embedding size
+ io.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
+
+ // For each row, we get the element values of each cell
+ for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+ // Read each range of cells of v_size_el length each into tmp_buf and write out
+ for (const auto & range : cell_ranges) {
+ const size_t range_size = range.second - range.first;
+ const size_t src_offset = (range.first + j * kv_size) * v_size_el;
+ const size_t buf_size = range_size * v_size_el;
+ io.write_tensor(v_l[il], src_offset, buf_size);
+ }
+ }
+ }
+ }
+}
+
+bool llama_kv_cache_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) {
+ if (dest_seq_id != -1) {
+ // single sequence
+
+ seq_rm(dest_seq_id, -1, -1);
+
+ llama_sbatch sbatch;
+ llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false);
+
+ batch.n_tokens = cell_count;
+ batch.n_seq_tokens = cell_count;
+ batch.n_seqs = 1;
+
+ for (uint32_t i = 0; i < cell_count; ++i) {
+ llama_pos pos;
+ uint32_t n_seq_id;
+
+ io.read_to(&pos, sizeof(pos));
+ io.read_to(&n_seq_id, sizeof(n_seq_id));
+
+ if (n_seq_id != 0) {
+ LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
+ return false;
+ }
+
+ batch.pos[i] = pos;
+ }
+ batch.n_seq_id[0] = 1;
+ batch.seq_id[0] = &dest_seq_id;
+ if (!find_slot(batch)) {
+ LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+ return false;
+ }
+ commit();
+
+ // DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
+ // Assume that this is one contiguous block of cells
+ GGML_ASSERT(head + cell_count <= size);
+ GGML_ASSERT(cells[head].pos == batch.pos[0]);
+ GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]);
+ GGML_ASSERT(cells[head].has_seq_id(dest_seq_id));
+ GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id));
+ } else {
+ // whole KV cache restore
+
+ if (cell_count > size) {
+ LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
+ return false;
+ }
+
+ clear();
+
+ for (uint32_t i = 0; i < cell_count; ++i) {
+ kv_cell & cell = cells[i];
+
+ llama_pos pos;
+ uint32_t n_seq_id;
+
+ io.read_to(&pos, sizeof(pos));
+ io.read_to(&n_seq_id, sizeof(n_seq_id));
+
+ cell.pos = pos;
+
+ for (uint32_t j = 0; j < n_seq_id; ++j) {
+ llama_seq_id seq_id;
+ io.read_to(&seq_id, sizeof(seq_id));
+
+ // TODO: llama_kv_cache_recurrent 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);
+ return false;
+ }
+
+ cell.seq_id.insert(seq_id);
+
+ int32_t & tail = cells[seq_id].tail;
+ if (tail != -1) {
+ LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail);
+ return false;
+ }
+ tail = i;
+ }
+ }
+
+ head = 0;
+ used = cell_count;
+ }
+
+ for (uint32_t i = 0; i < cell_count; ++i) {
+ uint32_t cell_id = head + i;
+ // make sure the recurrent states will keep their restored state
+ cells[cell_id].src = cell_id;
+ }
+
+ return true;
+}
+
+bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell_count) {
+ uint32_t v_trans;
+ uint32_t n_layer;
+ io.read_to(&v_trans, sizeof(v_trans));
+ io.read_to(&n_layer, sizeof(n_layer));
+
+ if (n_layer != hparams.n_layer) {
+ LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer);
+ return false;
+ }
+ if (cell_count > size) {
+ LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size);
+ return false;
+ }
+ if (false != (bool) v_trans) {
LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__);
return false;
}
view->cells_sequences = (llama_seq_id *)p;
}
- const std::vector<llama_kv_cell> & kv_cells = kvu->cells;
+ const std::vector<llama_kv_cache_unified::kv_cell> & kv_cells = kvu->cells;
llama_kv_cache_view_cell * c_curr = view->cells;
llama_seq_id * cs_curr = view->cells_sequences;
int32_t used_cells = 0;
#include "llama.h"
#include "llama-io.h"
+#include "llama-graph.h"
#include "llama-memory.h"
#include "ggml-cpp.h"
-#include <functional>
#include <set>
#include <vector>
struct llama_cparams;
struct llama_hparams;
struct llama_ubatch;
+struct llama_sbatch;
+struct llama_model;
+struct llama_context;
struct llama_kv_cache : public llama_memory_i {
- using llama_memory_i::llama_memory_i;
+ virtual ~llama_kv_cache() = default;
- virtual void restore() = 0; // call if batch processing fails - restores the cache state
- virtual void commit() = 0; // call after successful batch processing - clears any pending state
+ // call if batch processing fails - restores the cache state
+ virtual void restore() = 0;
- 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
+ // call after successful batch processing - clears any pending state
+ virtual void commit() = 0;
- virtual bool get_can_shift() const = 0;
+ // process any pending defrag/shift/etc. operations
+ // optionally call once before processing a new batch
+ virtual bool update(llama_context & lctx) = 0;
+
+ // schedule a defrag if the fragmentation threshold is exceeded. otherwise, do nothing
+ virtual void defrag_sched(float thold) = 0;
+
+ // simulate full cache, used for allocating worst-case compute buffers
+ virtual void set_full() = 0;
+
+ //
+ // batch processing
+ //
+
+ virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0;
+
+ // different KV caches require different batch splitting strategies
+ virtual llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const = 0;
+
+ // find an empty slot of size "n_tokens" in the cache
+ virtual bool find_slot(const llama_ubatch & batch) = 0;
+
+ // 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;
bool get_can_edit() const override { return get_can_shift(); }
+
+ //
+ // state write/read
+ //
+
+ virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
+ virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0;
};
+//
+// llama_kv_cache_guard
+//
+
struct llama_kv_cache_guard {
llama_kv_cache_guard(llama_kv_cache * kv) : kv(kv) {}
llama_kv_cache * kv;
};
-struct llama_kv_cell {
- llama_pos pos = -1;
- llama_pos delta = 0;
- int32_t src = -1; // used by recurrent state models to copy states
- int32_t tail = -1;
+//
+// llama_kv_cache_unified
+//
- std::set<llama_seq_id> seq_id;
+// TODO: add notion of max sequences
+class llama_kv_cache_unified : public llama_kv_cache {
+public:
+ struct kv_cell {
+ llama_pos pos = -1;
+ llama_pos delta = 0;
- bool has_seq_id(const llama_seq_id & id) const {
- return seq_id.find(id) != seq_id.end();
- }
+ std::set<llama_seq_id> seq_id;
- bool is_empty() const {
- return seq_id.empty();
- }
+ bool has_seq_id(const llama_seq_id & id) const {
+ return seq_id.find(id) != seq_id.end();
+ }
- bool is_same_seq(const llama_kv_cell & other) const {
- return seq_id == other.seq_id;
- }
-};
+ bool is_empty() const {
+ return seq_id.empty();
+ }
-// ring-buffer of cached KV data
-// TODO: pimpl
-// TODO: add notion of max sequences
-class llama_kv_cache_unified : public llama_kv_cache {
-public:
- // can be used to query data from the model if needed
- struct callbacks {
- std::function<ggml_tensor * (uint32_t n_ctx_per_seq, int il)> get_rope_factors;
+ bool is_same_seq(const kv_cell & other) const {
+ return seq_id == other.seq_id;
+ }
};
- llama_kv_cache_unified(
- const llama_hparams & hparams,
- callbacks cbs);
-
- virtual ~llama_kv_cache_unified() = default;
+ static uint32_t get_padding(const llama_cparams & cparams);
- // TODO: become constructor
- bool init(
- const llama_model & model, // TODO: do not reference the model
- const llama_cparams & cparams,
+ llama_kv_cache_unified(
+ const llama_model & model,
ggml_type type_k,
ggml_type type_v,
+ bool v_trans,
+ bool offload,
uint32_t kv_size,
- bool offload);
-
- int32_t get_n_tokens() const override;
- int32_t get_used_cells() const override;
+ uint32_t padding);
- size_t total_size() const;
+ ~llama_kv_cache_unified() = default;
- // TODO: better data structures to reduce the cost of this operation
- llama_pos pos_max() const;
+ //
+ // llama_memory_i
+ //
void clear() override;
- void defrag() override;
-
- virtual void restore() override;
- virtual void commit() override;
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;
llama_pos seq_pos_max(llama_seq_id seq_id) const override;
- bool get_can_shift() const override;
+ //
+ // llama_kv_cache
+ //
+
+ void restore() override;
+ void commit() override;
+
+ bool update(llama_context & ctx) override;
+
+ void defrag_sched(float thold) override;
+
+ void set_full() override;
+
+ llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
+
+ llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
- // find an empty slot of size "n_tokens" in the cache
// updates the cache head
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
- bool find_slot(const llama_ubatch & batch);
+ bool find_slot(const llama_ubatch & batch) override;
- // TODO: maybe not needed
- uint32_t get_padding(const llama_cparams & cparams) const;
+ int32_t get_n_tokens() const override;
+ int32_t get_used_cells() const override;
- // find how many cells are currently in use
- uint32_t cell_max() const;
+ // TODO: better data structures to reduce the cost of this operation
+ llama_pos get_pos_max() const override;
- size_t size_k_bytes() const;
- size_t size_v_bytes() const;
+ bool get_can_shift() const override;
- // defrag
+ // state write/load
+
+ void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
+ void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
+
+ // Note: The value of head isn't only used to optimize searching
+ // for a free KV slot. llama_decode_impl also uses it, so it
+ // cannot be freely changed after a slot has been allocated.
+ uint32_t head = 0;
+ uint32_t size = 0;
+ uint32_t used = 0; // used cells (i.e. at least one seq_id)
+
+ // computed before each graph build
+ uint32_t n = 0;
+
+ std::vector<kv_cell> cells;
+
+ std::vector<ggml_tensor *> k_l; // per layer
+ std::vector<ggml_tensor *> v_l;
+
+private:
+ const llama_model & model;
+ const llama_hparams & hparams;
+
+ bool has_shift = false;
+ bool do_defrag = false;
+
+ bool v_trans = true; // the value tensor is transposed
+ bool can_shift = false;
+
+ // required padding
+ uint32_t padding = 1;
+
+ ggml_type type_k = GGML_TYPE_F16;
+ ggml_type type_v = GGML_TYPE_F16;
+
+ std::vector<ggml_context_ptr> ctxs;
+ std::vector<ggml_backend_buffer_ptr> bufs;
+ // defrag
struct {
std::vector<uint32_t> ids;
} defrag_info;
bool defrag_prepare(int32_t n_max_nodes);
// commit/restore cache
-
struct slot_range {
uint32_t c0 = 0; // note: these are cell indices, not sequence positions
uint32_t c1 = 0;
std::vector<slot_range> ranges;
} pending;
- // state write/load
+ // find how many cells are currently in use
+ uint32_t cell_max() const;
- void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const;
- void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1);
+ size_t total_size() const;
- // members
+ size_t size_k_bytes() const;
+ size_t size_v_bytes() const;
- const llama_hparams & hparams;
+ ggml_tensor * build_rope_shift(
+ const llama_cparams & cparams,
+ ggml_context * ctx,
+ ggml_tensor * cur,
+ ggml_tensor * shift,
+ ggml_tensor * factors,
+ float freq_base,
+ float freq_scale) const;
+
+ llm_graph_result_ptr build_graph_shift(
+ const llama_cparams & cparams,
+ ggml_context * ctx,
+ ggml_cgraph * gf) const;
+
+ llm_graph_result_ptr build_graph_defrag(
+ const llama_cparams & cparams,
+ ggml_context * ctx,
+ ggml_cgraph * gf) const;
- callbacks cbs;
+ void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
+ void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
- bool has_shift = false;
- bool do_defrag = false;
+ bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
+ bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
+};
- // TODO: remove this and implement llama_kv_cache_recurrent instead
- bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
+//
+// llama_kv_cache_recurrent
+//
- bool v_trans = true; // the value tensor is transposed
- bool can_shift = false;
+class llama_kv_cache_recurrent : public llama_kv_cache {
+public:
+ struct kv_cell {
+ llama_pos pos = -1;
+ int32_t src = -1; // used to copy states
+ int32_t tail = -1;
+
+ std::set<llama_seq_id> seq_id;
+
+ bool has_seq_id(const llama_seq_id & id) const {
+ return seq_id.find(id) != seq_id.end();
+ }
+
+ bool is_empty() const {
+ return seq_id.empty();
+ }
+
+ bool is_same_seq(const kv_cell & other) const {
+ return seq_id == other.seq_id;
+ }
+ };
+
+ llama_kv_cache_recurrent(
+ const llama_model & model,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool offload,
+ uint32_t kv_size);
+
+ ~llama_kv_cache_recurrent() = default;
+
+ //
+ // llama_memory_i
+ //
+
+ void clear() override;
+
+ 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_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;
+
+ llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+ //
+ // llama_kv_cache
+ //
+
+ void restore() override;
+ void commit() override;
+
+ bool update(llama_context & lctx) override;
+
+ void defrag_sched(float thold) override;
+
+ void set_full() override;
+
+ llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
+
+ llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
+
+ 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
+ int32_t s_copy(int i) const;
+ float s_mask(int i) const;
+
+ // state write/load
+
+ void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
+ void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override;
// Note: The value of head isn't only used to optimize searching
// for a free KV slot. llama_decode_impl also uses it, so it
// computed before each graph build
uint32_t n = 0;
- std::vector<llama_kv_cell> cells;
+ std::vector<kv_cell> cells;
std::vector<ggml_tensor *> k_l; // per layer
std::vector<ggml_tensor *> v_l;
private:
+ //const llama_model & model;
+ const llama_hparams & hparams;
+
+ // commit/restore cache
+ // TODO: rework for recurrent cache
+ struct slot_range {
+ uint32_t c0 = 0; // note: these are cell indices, not sequence positions
+ uint32_t c1 = 0;
+ };
+
+ // pending cell updates that are not yet committed
+ struct {
+ std::vector<slot_range> ranges;
+ } pending;
+
ggml_type type_k = GGML_TYPE_F16;
ggml_type type_v = GGML_TYPE_F16;
std::vector<ggml_context_ptr> ctxs;
std::vector<ggml_backend_buffer_ptr> bufs;
+ // find how many cells are currently in use
+ uint32_t cell_max() const;
+
+ size_t total_size() const;
+
+ size_t size_k_bytes() const;
+ size_t size_v_bytes() const;
+
void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
-// TODO: temporary reusing llama_kv_cache_unified -- implement recurrent cache and simplify llama_kv_cache_unified
-//class llama_kv_cache_recurrent : public llama_kv_cache_unified {
-//public:
-// using llama_kv_cache_unified::llama_kv_cache_unified;
-//};
//
// kv cache view
overview / pkg / ggml / sources / llama.cpp / commitdiff