llama-impl.cpp
llama-io.cpp
llama-kv-cache.cpp
+ llama-kv-cache-unified.cpp
+ llama-kv-cache-unified-iswa.cpp
+ llama-kv-cache-recurrent.cpp
llama-memory.cpp
llama-mmap.cpp
llama-model-loader.cpp
{ LLM_KV_CONVNEXT_EMBEDDING_LENGTH, "%s.convnext.embedding_length" },
{ LLM_KV_CONVNEXT_BLOCK_COUNT, "%s.convnext.block_count" },
+ { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
+
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_POS_EMBD, "position_embd" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
LLM_KV_CONVNEXT_BLOCK_COUNT,
+ LLM_KV_CLASSIFIER_OUTPUT_LABELS,
+
// deprecated:
LLM_KV_TOKENIZER_PREFIX_ID,
LLM_KV_TOKENIZER_SUFFIX_ID,
break;
}
}
- ubatch_token.resize(!has_embd ? n_ubatch : 0);
- ubatch_embd.resize(has_embd ? n_embd * n_ubatch : 0);
- ubatch_pos.resize(n_ubatch);
- ubatch_n_seq_id.resize(n_ubatch);
- ubatch_seq_id.resize(n_ubatch);
- ubatch_output.resize(n_ubatch);
+
+ udatas.push_back({});
+
+ auto & udata = udatas.back();
+
+ udata.token.resize(!has_embd ? n_ubatch : 0);
+ udata.embd.resize(has_embd ? n_embd * n_ubatch : 0);
+ udata.pos.resize(n_ubatch);
+ udata.n_seq_id.resize(n_ubatch);
+ udata.seq_id.resize(n_ubatch);
+ udata.output.resize(n_ubatch);
+
llama_ubatch ubatch = {
/*equal_seqs =*/ true,
/*n_tokens =*/ 0,
/*n_seq_tokens =*/ 0,
/*n_seqs =*/ 0,
- /*token =*/ !has_embd ? ubatch_token.data() : nullptr,
- /*embd =*/ has_embd ? ubatch_embd.data() : nullptr,
- /*pos =*/ ubatch_pos.data(),
- /*n_seq_id =*/ ubatch_n_seq_id.data(),
- /*seq_id =*/ ubatch_seq_id.data(),
- /*output =*/ ubatch_output.data(),
+ /*token =*/ !has_embd ? udata.token.data() : nullptr,
+ /*embd =*/ has_embd ? udata.embd.data() : nullptr,
+ /*pos =*/ udata.pos.data(),
+ /*n_seq_id =*/ udata.n_seq_id.data(),
+ /*seq_id =*/ udata.seq_id.data(),
+ /*output =*/ udata.output.data(),
};
+
return ubatch;
}
bool equal_seqs;
// TODO: whole_seqs for embeddings?
- uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs)
+ uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs)
uint32_t n_seq_tokens; // tokens per sequence
uint32_t n_seqs;
llama_token * token; // [n_tokens]
float * embd; // [n_embd, n_tokens]
llama_pos * pos; // [n_tokens]
- int32_t * n_seq_id; // [n_seqs]
- llama_seq_id ** seq_id; // [n_seqs]
+ int32_t * n_seq_id; // [n_seqs] // TODO: remove, should belong to only 1 sequence
+ llama_seq_id ** seq_id; // [n_seqs] // TODO: become llama_seq_id * seq_id;
int8_t * output; // [n_tokens]
};
const llama_batch * batch = nullptr;
- // buffers for the ubatch
- std::vector<llama_token> ubatch_token;
- std::vector<float> ubatch_embd;
- std::vector<llama_pos> ubatch_pos;
- std::vector<int32_t> ubatch_n_seq_id;
- std::vector<llama_seq_id *> ubatch_seq_id;
- std::vector<int8_t> ubatch_output;
+ // buffers for the ubatches
+ // TODO: very hacky, this needs a complete rework
+ struct ubatch_data {
+ std::vector<llama_token> token;
+ std::vector<float> embd;
+ std::vector<llama_pos> pos;
+ std::vector<int32_t> n_seq_id;
+ std::vector<llama_seq_id *> seq_id;
+ std::vector<int8_t> output;
+ };
+
+ std::vector<ubatch_data> udatas;
llama_ubatch reserve_ubatch(size_t n_ubatch, bool has_embd = false);
#include "llama-model.h"
#include "llama-kv-cache.h"
+#include <cinttypes>
#include <cstring>
+#include <limits>
#include <stdexcept>
-#include <cinttypes>
//
// llama_context
__func__, n_ctx_per_seq, hparams.n_ctx_train);
}
+ if (!params.swa_full && cparams.n_seq_max > 1) {
+ LLAMA_LOG_WARN("%s: requested n_seq_max (%u) > 1, but swa_full is not enabled -- performance may be degraded: %s\n",
+ __func__, cparams.n_seq_max, "https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573");
+ }
+
if (!hparams.vocab_only) {
// GPU backends
for (auto * dev : model.devices) {
// reserve worst-case graph
if (!hparams.vocab_only && memory) {
- const uint32_t n_seqs = 1; // TODO: worst-case number of sequences
+ const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
- llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
-
- // restore later
- // TODO: something cleaner
- const auto n_outputs_save = n_outputs;
-
LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
int n_splits_pp = -1;
// simulate full KV cache
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
- kv_self->set_full();
+ const auto kv_state = kv_self->init_full();
+ if (!kv_state) {
+ throw std::runtime_error("failed to initialize KV cache");
+ }
cross.v_embd.clear();
// reserve pp graph first so that buffers are only allocated once
{
- llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-
- // max number of outputs
- n_outputs = ubatch_pp.n_tokens;
-
- LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
-
- auto * gf = graph_init();
- graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
-
- if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+ auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, kv_state.get());
+ if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
// reserve with tg graph to get the number of splits and nodes
{
- llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-
- n_outputs = ubatch_tg.n_tokens;
-
- LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_tg.n_tokens, ubatch_tg.n_seqs);
-
- auto * gf = graph_init();
- graph_build(ctx_compute.get(), gf, ubatch_tg, LLM_GRAPH_TYPE_DEFAULT);
-
- if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+ auto * gf = graph_reserve(1, 1, 1, kv_state.get());
+ if (!gf) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
// reserve again with pp graph to avoid ggml-alloc reallocations during inference
{
- llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
-
- n_outputs = ubatch_pp.n_tokens;
-
- LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
-
- auto * gf = graph_init();
- graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
-
- if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+ auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, kv_state.get());
+ if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
}
- n_outputs = n_outputs_save;
-
for (size_t i = 0; i < backend_ptrs.size(); ++i) {
ggml_backend_t backend = backend_ptrs[i];
ggml_backend_buffer_type_t buft = backend_buft[i];
return kv_self;
}
-void llama_context::kv_self_update() {
- bool need_reserve = false;
+bool llama_context::kv_self_update() {
+ if (!memory) {
+ return false;
+ }
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
- need_reserve = kv_self->update(*this);
-
- // reserve a worst case graph if needed
- if (need_reserve) {
- LLAMA_LOG_DEBUG("%s: reserving a worst case graph\n", __func__);
-
- // build worst-case graph
- uint32_t n_seqs = 1; // TODO: worst-case number of sequences
- uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
-
- // simulate full KV cache
- kv_self->set_full();
+ if (!kv_self->update(*this)) {
+ // no updates have been performed
+ return false;
+ }
- 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};
+ // if the KV cache did any computation, we have to reserve a new worst-case graph
+ const auto kv_state = kv_self->init_full();
+ if (!kv_state) {
+ throw std::runtime_error("failed to initialize KV cache");
+ }
- auto * gf = graph_init();
- graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT);
+ const uint32_t n_seqs = cparams.n_seq_max;
+ const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
- // initialize scheduler with the worst-case graph
- ggml_backend_sched_reset(sched.get());
- if (!ggml_backend_sched_reserve(sched.get(), gf)) {
- LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
- }
+ auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, kv_state.get());
+ if (!gf) {
+ LLAMA_LOG_ERROR("%s: failed to reserve graph after the KV cache update\n", __func__);
}
+
+ return true;
}
enum llama_pooling_type llama_context::pooling_type() const {
return cvec.apply(model, data, len, n_embd, il_start, il_end);
}
+llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_state_i * mstate, ggml_status & ret) {
+ if (mstate && !mstate->apply()) {
+ LLAMA_LOG_ERROR("%s: failed to apply memory state\n", __func__);
+ ret = GGML_STATUS_FAILED;
+ return nullptr;
+ }
+
+ auto * gf = graph_init();
+ if (!gf) {
+ LLAMA_LOG_ERROR("%s: failed to initialize graph\n", __func__);
+ ret = GGML_STATUS_FAILED;
+ return nullptr;
+ }
+
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mstate);
+ if (!res) {
+ LLAMA_LOG_ERROR("%s: failed to build graph\n", __func__);
+ ret = GGML_STATUS_FAILED;
+ return nullptr;
+ }
+
+ // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+ if (!ggml_backend_sched_alloc_graph(sched.get(), gf)) {
+ LLAMA_LOG_ERROR("%s: failed to allocate graph\n", __func__);
+ ret = GGML_STATUS_ALLOC_FAILED;
+ return nullptr;
+ }
+
+ res->set_inputs(&ubatch);
+
+ const auto status = graph_compute(gf, ubatch.n_tokens > 1);
+ if (status != GGML_STATUS_SUCCESS) {
+ LLAMA_LOG_ERROR("%s: failed to compute graph, compute status: %d\n", __func__, status);
+ ret = status;
+ return nullptr;
+ }
+
+ ret = GGML_STATUS_SUCCESS;
+
+ return res;
+}
+
int llama_context::encode(llama_batch & inp_batch) {
if (inp_batch.n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
n_outputs = n_tokens;
- //batch_manager->prepare(ubatch);
-
ggml_backend_sched_reset(sched.get());
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
// ref: https://github.com/ggml-org/llama.cpp/pull/12181#issuecomment-2730451223
cparams.causal_attn = false;
- auto * gf = graph_init();
- auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_ENCODER);
-
- ggml_backend_sched_alloc_graph(sched.get(), gf);
-
- res->set_inputs(&ubatch);
+ ggml_status status;
+ const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_ENCODER, nullptr, status);
cparams.causal_attn = causal_attn_org;
- const auto compute_status = graph_compute(gf, n_tokens > 1);
- switch (compute_status) {
- case GGML_STATUS_SUCCESS:
- break;
- case GGML_STATUS_ABORTED:
- return 2;
- case GGML_STATUS_ALLOC_FAILED:
- return -2;
- case GGML_STATUS_FAILED:
- default:
- return -3;
+ if (!res) {
+ switch (status) {
+ case GGML_STATUS_ABORTED: return 2;
+ case GGML_STATUS_ALLOC_FAILED: return -2;
+ case GGML_STATUS_FAILED: return -3;
+ case GGML_STATUS_SUCCESS: GGML_ABORT("should not happen");
+ }
}
auto * t_embd = res->get_embd_pooled() ? res->get_embd_pooled() : res->get_embd();
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);
-
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
// TODO: move the validation to the llama_batch_allocr
n_outputs_all = 1;
}
- llama_sbatch sbatch = kv_self->sbatch_init(batch, /* logits_all */ n_outputs_all == n_tokens_all);
+ // handle any pending defrags/shifts
+ kv_self_update();
+
+ llama_memory_state_ptr kv_state;
+
+ bool did_defrag = false;
+
+ while (true) {
+ kv_state = kv_self->init_batch(batch, cparams.n_ubatch, embd_pooled, /* logits_all */ n_outputs_all == n_tokens_all);
+ if (!kv_state) {
+ return -2;
+ }
+
+ switch (kv_state->get_status()) {
+ case LLAMA_MEMORY_STATUS_SUCCESS:
+ {
+ } break;
+ case LLAMA_MEMORY_STATUS_FAILED_PREPARE:
+ {
+ if (!did_defrag) {
+ did_defrag = true;
+
+ kv_self->defrag_sched(-1.0f);
+ if (kv_self_update()) {
+ LLAMA_LOG_DEBUG("%s: failed to init batch of size %d, retrying after defrag\n", __func__, batch.n_tokens);
+
+ continue;
+ }
+ }
+
+ LLAMA_LOG_WARN("%s: failed to find KV cache slot for batch of size %d\n", __func__, batch.n_tokens);
+
+ return 1;
+ }
+ case LLAMA_MEMORY_STATUS_FAILED_COMPUTE:
+ {
+ return -2;
+ }
+ }
+
+ break;
+ }
// reserve output buffer
if (output_reserve(n_outputs_all) < n_outputs_all) {
return -2;
};
- // handle any pending defrags/shifts
- kv_self_update();
-
int64_t n_outputs_prev = 0;
- while (sbatch.n_tokens > 0) {
- llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
+ do {
+ const auto & ubatch = kv_state->get_ubatch();
// count the outputs in this u_batch
{
n_outputs = n_outputs_new;
}
- // find KV slot
- if (!kv_self->find_slot(ubatch)) {
- return 1;
- }
-
ggml_backend_sched_reset(sched.get());
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
- auto * gf = graph_init();
- auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DECODER);
+ ggml_status status;
+ const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, kv_state.get(), status);
+
+ if (!res) {
+ // the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
+ llama_pos pos_min[LLAMA_MAX_PARALLEL_SEQUENCES] = { std::numeric_limits<llama_pos>::max() };
- // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+ for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+ const auto & seq_id = ubatch.seq_id[i][0];
- ggml_backend_sched_alloc_graph(sched.get(), gf);
+ pos_min[seq_id] = std::min(pos_min[seq_id], ubatch.pos[i]);
+ }
- res->set_inputs(&ubatch);
+ for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+ if (pos_min[s] == std::numeric_limits<llama_pos>::max()) {
+ continue;
+ }
- const auto compute_status = graph_compute(gf, ubatch.n_tokens > 1);
- if (compute_status != GGML_STATUS_SUCCESS) {
- switch (compute_status) {
- case GGML_STATUS_ABORTED:
- return 2;
- case GGML_STATUS_ALLOC_FAILED:
- return -2;
- case GGML_STATUS_FAILED:
- default:
- return -3;
+ LLAMA_LOG_WARN("%s: removing KV cache entries for seq_id = %d, pos = [%d, +inf)\n", __func__, s, pos_min[s]);
+
+ llama_kv_self_seq_rm(this, s, pos_min[s], -1);
+ }
+
+ switch (status) {
+ case GGML_STATUS_ABORTED: return 2;
+ case GGML_STATUS_ALLOC_FAILED: return -2;
+ case GGML_STATUS_FAILED: return -3;
+ case GGML_STATUS_SUCCESS: GGML_ABORT("should not happen");
}
}
}
n_outputs_prev += n_outputs;
- }
-
- // finalize the batch processing
- kv_guard.commit();
+ } while (kv_state->next());
// set to total number of outputs in the batch, for use in llama_get_logits_ith
n_outputs = n_outputs_all;
{
bool sorted_output = true;
- auto & out_ids = sbatch.out_ids;
+ auto & out_ids = kv_state->out_ids();
GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all);
return ggml_new_graph_custom(ctx_compute.get(), graph_max_nodes(), false);
}
+ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate) {
+ LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
+
+ if (n_tokens % n_seqs != 0) {
+ n_tokens = (n_tokens / n_seqs) * n_seqs;
+ n_outputs = std::min(n_outputs, n_tokens);
+
+ LLAMA_LOG_DEBUG("%s: making n_tokens a multiple of n_seqs - n_tokens = %u, n_seqs = %u, n_outputs = %u\n", __func__, n_tokens, n_seqs, n_outputs);
+ }
+
+ // store the n_outputs as it is, and restore it afterwards
+ // TODO: not sure if needed, might simplify in the future by removing this
+ const auto save_n_outputs = this->n_outputs;
+
+ this->n_outputs = n_outputs;
+
+ 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};
+
+ auto * gf = graph_init();
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate);
+
+ this->n_outputs = save_n_outputs;
+
+ if (!res) {
+ LLAMA_LOG_ERROR("%s: failed to build worst-case graph\n", __func__);
+ return nullptr;
+ }
+
+ ggml_backend_sched_reset(sched.get());
+
+ // initialize scheduler with the specified graph
+ if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+ LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
+ return nullptr;
+ }
+
+ return gf;
+}
+
llm_graph_result_ptr llama_context::graph_build(
- ggml_context * ctx,
- ggml_cgraph * gf,
- const llama_ubatch & ubatch,
- llm_graph_type gtype) {
+ ggml_context * ctx,
+ ggml_cgraph * gf,
+ const llama_ubatch & ubatch,
+ llm_graph_type gtype,
+ const llama_memory_state_i * mstate) {
return model.build_graph(
{
/*.ctx =*/ ctx,
/*.backend_cpu =*/ backend_cpu,
/*.cvec =*/ &cvec,
/*.loras =*/ &loras,
- /*.memory =*/ memory.get(),
+ /*.mstate =*/ mstate,
/*.cross =*/ &cross,
/*.n_outputs =*/ n_outputs,
/*.cb =*/ graph_get_cb(),
llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
kv_self->clear();
- llama_kv_cache_guard kv_guard(kv_self);
for (uint32_t pos_ctx = 0; pos_ctx < n_ctx; pos_ctx += n_batch) {
batch.n_tokens = n_batch;
int64_t n_outputs_all = n_tokens_all;
- llama_sbatch sbatch = kv_self->sbatch_init(batch, /*logits_all =*/ true);
+ auto kv_state = kv_self->init_batch(batch, cparams.n_ubatch, embd_pooled, /* logits_all */ true);
+ if (!kv_state || kv_state->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
+ LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
+ break;
+ }
// reserve output buffer
if (output_reserve(n_outputs_all) < n_outputs_all) {
GGML_ABORT("TODO: handle this error");
};
- for (uint32_t pos_batch = 0; pos_batch < n_batch; pos_batch += n_ubatch) {
- llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
+ uint32_t pos_batch = 0;
+ do {
+ const auto & ubatch = kv_state->get_ubatch();
n_outputs = ubatch.n_tokens;
- // TODO: not sure if this is needed
- 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);
-
- GGML_ABORT("TODO: handle this error");
+ if (!kv_state->apply()) {
+ LLAMA_LOG_ERROR("%s: failed to update the memory state\n", __func__);
+ break;
}
auto * gf = graph_init();
- auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT);
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, kv_state.get());
struct ggml_context * ctx_compute_opt;
{
}
ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_tokens(), res->get_logits());
ggml_opt_alloc(opt_ctx, train);
+
res->set_inputs(&ubatch);
{
struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
callback(train, opt_ctx, dataset, result, idata_in_loop + (pos_ctx + pos_batch)/n_ubatch + 1, ndata_in_loop, t_loop_start);
}
ggml_free(ctx_compute_opt);
- }
- }
- kv_guard.commit();
+ pos_batch += ubatch.n_tokens;
+ } while (kv_state->next());
+ }
}
void llama_context::opt_epoch(
return ctx->get_kv_self();
}
+// deprecated
void llama_kv_self_update(llama_context * ctx) {
ctx->kv_self_update();
}
return kv->seq_pos_max(seq_id);
}
+// deprecated
void llama_kv_self_defrag(llama_context * ctx) {
auto * kv = ctx->get_kv_self();
if (!kv) {
int32_t llama_decode(
llama_context * ctx,
llama_batch batch) {
- int ret = ctx->decode(batch);
-
- // defrag and try again
- // TODO: distinguish return code when we are sure that even after defrag there is no space available
- if (ret == 1) {
- llama_kv_self_defrag(ctx);
- ret = ctx->decode(batch);
-
- if (ret == 1) {
- LLAMA_LOG_WARN("%s: failed to find KV cache slot for batch of size %d\n", __func__, batch.n_tokens);
-
- return ret;
- }
- }
-
- if (ret != 0) {
+ const int ret = ctx->decode(batch);
+ if (ret != 0 && ret != 1) {
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
}
class llama_io_read_i;
class llama_io_write_i;
+class llama_memory_i;
+class llama_memory_state_i;
+
struct llama_context {
// init scheduler and compute buffers, reserve worst-case graphs
llama_context(
llama_kv_cache * get_kv_self();
const llama_kv_cache * get_kv_self() const;
- void kv_self_update();
+ // return true of the KV cache was updated
+ // TODO: remove
+ bool kv_self_update();
enum llama_pooling_type pooling_type() const;
int32_t il_start,
int32_t il_end);
+ // process a single ubatch with a specific graph type
+ // if memory_state is provided, it will be applied first to the context's memory
+ // ret contains the status of the graph computation
+ // returns nullptr only if ret != GGML_STATUS_SUCCESS
+ llm_graph_result_ptr process_ubatch(
+ const llama_ubatch & ubatch,
+ llm_graph_type gtype,
+ llama_memory_state_i * mstate,
+ ggml_status & ret);
+
int encode(llama_batch & inp_batch);
int decode(llama_batch & inp_batch);
ggml_cgraph * graph_init();
// returns the result of ggml_backend_sched_graph_compute_async execution
- ggml_status graph_compute(
- ggml_cgraph * gf,
- bool batched);
+ ggml_status graph_compute(ggml_cgraph * gf, bool batched);
+
+ // reserve a graph with a dummy ubatch of the specified size
+ ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate);
private:
llm_graph_result_ptr graph_build(
- ggml_context * ctx,
- ggml_cgraph * gf,
- const llama_ubatch & ubatch,
- llm_graph_type gtype);
+ ggml_context * ctx,
+ ggml_cgraph * gf,
+ const llama_ubatch & ubatch,
+ llm_graph_type gtype,
+ const llama_memory_state_i * mstate);
llm_graph_cb graph_get_cb() const;
#include "llama-impl.h"
#include "llama-batch.h"
#include "llama-cparams.h"
-#include "llama-kv-cache.h"
+
+#include "llama-kv-cache-unified.h"
+#include "llama-kv-cache-unified-iswa.h"
+#include "llama-kv-cache-recurrent.h"
#include <cassert>
#include <cmath>
void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) {
if (pos_bucket) {
- kv_self->set_input_pos_bucket(pos_bucket, ubatch);
+ kv_state->set_input_pos_bucket(pos_bucket, ubatch);
}
}
void llm_graph_input_s_copy::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
- const int64_t n_kv = kv_self->n;
+ const int64_t n_kv = kv_state->get_n_kv();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_kv; ++i) {
- data[i] = kv_self->s_copy(i);
+ data[i] = kv_state->s_copy(i);
}
}
}
void llm_graph_input_s_mask::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
- const int64_t n_kv = kv_self->n;
+ const int64_t n_kv = kv_state->get_n_kv();
if (s_mask) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_mask->buffer));
// clear unused states
for (int i = 0; i < n_kv; ++i) {
- data[i] = kv_self->s_mask(i);
+ data[i] = kv_state->s_mask(i);
}
}
}
void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
- kv_self->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
- kv_self->get_kv_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ kv_state->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
if (self_kq_mask_swa) {
- kv_self->get_kv_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
+ kv_state->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
}
}
backend_cpu (params.backend_cpu),
cvec (params.cvec),
loras (params.loras),
- memory (params.memory),
+ mstate (params.mstate),
cross (params.cross),
cb_func (params.cb),
res (std::make_unique<llm_graph_result>()) {
}
int64_t llm_graph_context::n_pos_per_embd() const {
- return arch == LLM_ARCH_QWEN2VL ? 4 : 1;
+ return hparams.rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
}
void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
}
ggml_tensor * llm_graph_context::build_inp_s_copy() const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
- auto inp = std::make_unique<llm_graph_input_s_copy>(kv_self);
+ auto inp = std::make_unique<llm_graph_input_s_copy>(kv_state);
- const auto n_kv = kv_self->n;
+ const auto n_kv = kv_state->get_n_kv();
auto & cur = inp->s_copy;
}
ggml_tensor * llm_graph_context::build_inp_s_mask() const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
- auto inp = std::make_unique<llm_graph_input_s_mask>(kv_self);
+ auto inp = std::make_unique<llm_graph_input_s_mask>(kv_state);
- const auto n_kv = kv_self->n;
+ const auto n_kv = kv_state->get_n_kv();
auto & cur = inp->s_mask;
}
ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
- const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
- auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, kv_self);
+ auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, kv_state);
- const auto n_kv = kv_self->get_n();
+ const auto n_kv = kv_state->get_n_kv();
auto & cur = inp->pos_bucket;
}
llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
- const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
- auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, kv_self);
+ auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, kv_state);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
- const auto n_kv = kv_self->get_n();
+ const auto n_kv = kv_state->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
- const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
// store to KV cache
{
- ggml_build_forward_expand(gf, kv_self->cpy_k(ctx0, k_cur, il));
- ggml_build_forward_expand(gf, kv_self->cpy_v(ctx0, v_cur, il));
+ ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
+ ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
- ggml_tensor * k = kv_self->get_k(ctx0, il);
- ggml_tensor * v = kv_self->get_v(ctx0, il);
+ ggml_tensor * k = kv_state->get_k(ctx0, il);
+ ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
}
llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
- const llama_kv_cache_unified_iswa * kv_self = static_cast<const llama_kv_cache_unified_iswa *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
- auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, kv_self);
+ auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, kv_state);
{
- const auto n_kv = kv_self->get_kv_base()->get_n();
+ const auto n_kv = kv_state->get_base()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified for non-SWA");
- const auto n_kv = kv_self->get_kv_swa()->get_n();
+ const auto n_kv = kv_state->get_swa()->get_n_kv();
inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
- const bool is_swa = hparams.is_swa(il);
+ const auto * kv_state_iswa = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
- const llama_kv_cache_unified_iswa * kv_self = static_cast<const llama_kv_cache_unified_iswa *>(memory);
+ const bool is_swa = hparams.is_swa(il);
- const auto * kv = is_swa ? kv_self->get_kv_swa() : kv_self->get_kv_base();
+ const auto * kv_state = is_swa ? kv_state_iswa->get_swa() : kv_state_iswa->get_base();
// store to KV cache
{
- ggml_build_forward_expand(gf, kv->cpy_k(ctx0, k_cur, il));
- ggml_build_forward_expand(gf, kv->cpy_v(ctx0, v_cur, il));
+ ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
+ ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
ggml_tensor * q = q_cur;
- ggml_tensor * k = kv->get_k(ctx0, il);
- ggml_tensor * v = kv->get_v(ctx0, il);
+ ggml_tensor * k = kv_state->get_k(ctx0, il);
+ ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
ggml_tensor * state_mask,
int32_t n_state,
int32_t n_seqs) const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
- const auto n_kv = kv_self->n;
- const auto kv_head = kv_self->head;
+ const auto n_kv = kv_state->get_n_kv();
+ const auto kv_head = kv_state->get_head();
- ggml_tensor * states = ggml_reshape_2d(ctx0, s, n_state, kv_self->size);
+ ggml_tensor * states = ggml_reshape_2d(ctx0, s, n_state, kv_state->get_size());
// copy states
// NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
const auto token_shift_count = hparams.token_shift_count;
const int64_t n_seqs = ubatch.n_seqs;
- ggml_tensor * token_shift_all = kv_self->k_l[il];
+ ggml_tensor * token_shift_all = kv_state->get_k_l(il);
ggml_tensor * token_shift = build_copy_mask_state(
gf, token_shift_all, state_copy, state_mask,
ggml_tensor * token_shift,
const llama_ubatch & ubatch,
int il) const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
const auto token_shift_count = hparams.token_shift_count;
const auto n_embd = hparams.n_embd;
const int64_t n_seqs = ubatch.n_seqs;
- const auto kv_head = kv_self->head;
+ const auto kv_head = kv_state->get_head();
return ggml_cpy(
ctx0,
ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * token_shift_count, 0),
- ggml_view_1d(ctx0, kv_self->k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self->k_l[il]))
+ ggml_view_1d(ctx0, kv_state->get_k_l(il), hparams.n_embd_k_s()*n_seqs, hparams.n_embd_k_s()*kv_head*ggml_element_size(kv_state->get_k_l(il)))
);
}
ggml_tensor * inp_cls = build_inp_cls();
inp = ggml_get_rows(ctx0, inp, inp_cls);
- // classification head
- // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
- GGML_ASSERT(cls != nullptr);
- GGML_ASSERT(cls_b != nullptr);
-
- cur = ggml_add (ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
- cur = ggml_tanh(ctx0, cur);
-
- // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
- // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
- if (cls_out) {
+ if (cls != nullptr && cls_b != nullptr) {
+ // classification head
+ // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
+ cur = ggml_tanh(ctx0, cur);
+
+ // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
+ // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
+ if (cls_out) {
+ GGML_ASSERT(cls_out_b != nullptr);
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
+ }
+ } else if (cls_out) {
+ // Single layer classification head (direct projection)
+ // https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/bert/modeling_bert.py#L1476
GGML_ASSERT(cls_out_b != nullptr);
-
- cur = ggml_add (ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
+ cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, inp), cls_out_b);
+ } else {
+ GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
}
} break;
default:
struct llama_ubatch;
struct llama_cparams;
-class llama_memory_i;
-class llama_kv_cache_unified;
-class llama_kv_cache_unified_iswa;
-class llama_kv_cache_recurrent;
+class llama_memory_state_i;
+
+class llama_kv_cache_unified_state;
+class llama_kv_cache_unified_iswa_state;
+class llama_kv_cache_recurrent_state;
// certain models (typically multi-modal) can produce different types of graphs
enum llm_graph_type {
public:
llm_graph_input_pos_bucket_kv(
const llama_hparams & hparams,
- const llama_kv_cache_unified * kv_self) : hparams(hparams), kv_self(kv_self) {}
+ const llama_kv_cache_unified_state * kv_state) : hparams(hparams), kv_state(kv_state) {}
virtual ~llm_graph_input_pos_bucket_kv() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
const llama_hparams & hparams;
- const llama_kv_cache_unified * kv_self;
+ const llama_kv_cache_unified_state * kv_state;
};
class llm_graph_input_out_ids : public llm_graph_input_i {
class llm_graph_input_s_copy : public llm_graph_input_i {
public:
- llm_graph_input_s_copy(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {}
+ llm_graph_input_s_copy(const llama_kv_cache_recurrent_state * kv_state) : kv_state(kv_state) {}
virtual ~llm_graph_input_s_copy() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_copy; // I32 [kv_size]
- const llama_kv_cache_recurrent * kv_self;
+ const llama_kv_cache_recurrent_state * kv_state;
};
class llm_graph_input_s_mask : public llm_graph_input_i {
public:
- llm_graph_input_s_mask(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {}
+ llm_graph_input_s_mask(const llama_kv_cache_recurrent_state * kv_state) : kv_state(kv_state) {}
virtual ~llm_graph_input_s_mask() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_mask; // F32 [1, n_kv]
- const llama_kv_cache_recurrent * kv_self;
+ const llama_kv_cache_recurrent_state * kv_state;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
llm_graph_input_attn_kv_unified(
const llama_hparams & hparams,
const llama_cparams & cparams,
- const llama_kv_cache_unified * kv_self) :
+ const llama_kv_cache_unified_state * kv_state) :
hparams(hparams),
cparams(cparams),
- kv_self(kv_self) {
+ kv_state(kv_state) {
}
~llm_graph_input_attn_kv_unified() = default;
const llama_hparams & hparams;
const llama_cparams & cparams;
- const llama_kv_cache_unified * kv_self;
+ const llama_kv_cache_unified_state * kv_state;
};
class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
llm_graph_input_attn_kv_unified_iswa(
const llama_hparams & hparams,
const llama_cparams & cparams,
- const llama_kv_cache_unified_iswa * kv_self) :
+ const llama_kv_cache_unified_iswa_state * kv_state) :
hparams(hparams),
cparams(cparams),
- kv_self(kv_self) {
+ kv_state(kv_state) {
}
~llm_graph_input_attn_kv_unified_iswa() = default;
const llama_hparams & hparams;
const llama_cparams & cparams;
- const llama_kv_cache_unified_iswa * kv_self;
+ const llama_kv_cache_unified_iswa_state * kv_state;
};
class llm_graph_input_attn_cross : public llm_graph_input_i {
ggml_backend_sched_t sched;
ggml_backend_t backend_cpu;
- const llama_adapter_cvec * cvec;
- const llama_adapter_loras * loras;
- const llama_memory_i * memory;
- const llama_cross * cross;
+ const llama_adapter_cvec * cvec;
+ const llama_adapter_loras * loras;
+ const llama_memory_state_i * mstate;
+ const llama_cross * cross;
int32_t n_outputs;
ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
- const llama_adapter_cvec * cvec;
- const llama_adapter_loras * loras;
- const llama_memory_i * memory;
- const llama_cross * cross;
+ const llama_adapter_cvec * cvec;
+ const llama_adapter_loras * loras;
+ const llama_memory_state_i * mstate;
+ const llama_cross * cross;
const llm_graph_cb & cb_func;
bool attn_soft_cap = false;
bool use_kq_norm = true;
+ // for Classifiers
+ uint32_t n_cls_out = 1;
+
// llama4
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;
--- /dev/null
+#include "llama-kv-cache-recurrent.h"
+
+#include "llama-impl.h"
+#include "llama-batch.h"
+#include "llama-model.h"
+
+#include <algorithm>
+#include <cassert>
+#include <limits>
+#include <map>
+#include <stdexcept>
+
+//
+// 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,
+ uint32_t n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
+ const int32_t n_layer = hparams.n_layer;
+
+ LLAMA_LOG_INFO("%s: kv_size = %u, n_seq_max = %u, type_k = '%s', type_v = '%s', n_layer = %d\n",
+ __func__, kv_size, n_seq_max, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
+
+ head = 0;
+ size = kv_size;
+ used = 0;
+
+ 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 shift) {
+ if (shift == 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 += shift;
+ }
+ }
+ }
+}
+
+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_min(llama_seq_id seq_id) const {
+ llama_pos result = std::numeric_limits<llama_pos>::max();
+
+ for (uint32_t i = 0; i < size; ++i) {
+ if (cells[i].has_seq_id(seq_id)) {
+ result = std::min(result, cells[i].pos);
+ }
+ }
+
+ if (result == std::numeric_limits<llama_pos>::max()) {
+ result = -1;
+ }
+
+ return result;
+}
+
+llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const {
+ llama_pos result = -1;
+
+ 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;
+}
+
+llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
+ GGML_UNUSED(embd_pooled);
+
+ auto sbatch = llama_sbatch(batch, hparams.n_embd, false, logits_all);
+
+ std::vector<llama_ubatch> ubatches;
+
+ while (sbatch.n_tokens > 0) {
+ llama_ubatch ubatch;
+
+ if (embd_pooled) {
+ // Pooled embeddings cannot be split across ubatches (yet)
+ ubatch = sbatch.split_seq(n_ubatch);
+ } else {
+ ubatch = sbatch.split_equal(n_ubatch);
+ }
+
+ ubatches.push_back(ubatch);
+ }
+
+ if (!prepare(ubatches)) {
+ return std::make_unique<llama_kv_cache_recurrent_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ }
+
+ return std::make_unique<llama_kv_cache_recurrent_state>(LLAMA_MEMORY_STATUS_SUCCESS, this, std::move(sbatch), std::move(ubatches));
+}
+
+llama_memory_state_ptr llama_kv_cache_recurrent::init_full() {
+ return std::make_unique<llama_kv_cache_recurrent_state>(LLAMA_MEMORY_STATUS_SUCCESS, this);
+}
+
+bool llama_kv_cache_recurrent::prepare(const std::vector<llama_ubatch> & ubatches) {
+ // simply remember the full state because it is very small for this type of cache
+ // TODO: optimize
+ auto org_cells = cells;
+ auto org_used = used;
+ auto org_head = head;
+
+ bool success = true;
+
+ // TODO: here we have to verify that all ubatches can fit in the cells
+ // however, the current implementation is broken because it relies on s_copy() and s_mask() to update the cells
+ // during the compute of each ubatch. to reproduce, uncomment the following loop and run:
+ //
+ // $ llama-parallel -m ./mamba-130m/ggml-model-f16.gguf -np 5 -ns 8
+ //
+ // recovery from failures when the batch does not fit in the KV cache will not work correctly until this is fixed
+ //
+ GGML_UNUSED(ubatches);
+ //for (const auto & ubatch : ubatches) {
+ // if (!find_slot(ubatch)) {
+ // success = false;
+ // break;
+ // }
+ //}
+
+ // restore the original state
+ cells = std::move(org_cells);
+ used = org_used;
+ head = org_head;
+
+ return success;
+}
+
+bool llama_kv_cache_recurrent::update(llama_context & lctx) {
+ GGML_UNUSED(lctx);
+ // noop
+ return false;
+}
+
+void llama_kv_cache_recurrent::defrag_sched(float thold) {
+ GGML_UNUSED(thold);
+ // noop
+}
+
+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=%u Try using a bigger --parallel value\n", __func__, seq_id, n_seq_max);
+ 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;
+}
+
+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;
+}
+
+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;
+ }
+
+ // 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;
+ }
+
+ // 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 (!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_state
+//
+
+llama_kv_cache_recurrent_state::llama_kv_cache_recurrent_state(llama_memory_status status) : status(status) {}
+
+llama_kv_cache_recurrent_state::llama_kv_cache_recurrent_state(
+ llama_memory_status status,
+ llama_kv_cache_recurrent * kv) : status(status), kv(kv), is_full(true) {
+}
+
+llama_kv_cache_recurrent_state::llama_kv_cache_recurrent_state(
+ llama_memory_status status,
+ llama_kv_cache_recurrent * kv,
+ llama_sbatch sbatch,
+ std::vector<llama_ubatch> ubatches) : status(status), kv(kv), sbatch(std::move(sbatch)), ubatches(std::move(ubatches)) {}
+
+llama_kv_cache_recurrent_state::~llama_kv_cache_recurrent_state() = default;
+
+bool llama_kv_cache_recurrent_state::next() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ if (++i_next >= ubatches.size()) {
+ return false;
+ }
+
+ return true;
+}
+
+bool llama_kv_cache_recurrent_state::apply() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ kv->find_slot(ubatches[i_next]);
+
+ return true;
+}
+
+std::vector<int64_t> & llama_kv_cache_recurrent_state::out_ids() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return sbatch.out_ids;
+}
+
+llama_memory_status llama_kv_cache_recurrent_state::get_status() const {
+ return status;
+}
+
+const llama_ubatch & llama_kv_cache_recurrent_state::get_ubatch() const {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return ubatches[i_next];
+}
+
+uint32_t llama_kv_cache_recurrent_state::get_n_kv() const {
+ return is_full ? kv->size : kv->n;
+}
+
+uint32_t llama_kv_cache_recurrent_state::get_head() const {
+ return is_full ? 0 : kv->head;
+}
+
+uint32_t llama_kv_cache_recurrent_state::get_size() const {
+ return kv->size;
+}
+
+ggml_tensor * llama_kv_cache_recurrent_state::get_k_l(int32_t il) const {
+ return kv->k_l[il];
+}
+
+ggml_tensor * llama_kv_cache_recurrent_state::get_v_l(int32_t il) const {
+ return kv->v_l[il];
+}
+
+int32_t llama_kv_cache_recurrent_state::s_copy(int i) const {
+ return kv->s_copy(i);
+}
+
+float llama_kv_cache_recurrent_state::s_mask(int i) const {
+ return kv->s_mask(i);
+}
--- /dev/null
+#pragma once
+
+#include "llama-batch.h"
+#include "llama-graph.h"
+#include "llama-kv-cache.h"
+
+#include <set>
+#include <vector>
+
+//
+// llama_kv_cache_recurrent
+//
+
+// TODO: extract the KV cache state used for graph computation into llama_kv_cache_recurrent_state_i
+// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
+class llama_kv_cache_recurrent : public llama_kv_cache {
+public:
+ llama_kv_cache_recurrent(
+ const llama_model & model,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool offload,
+ uint32_t kv_size,
+ uint32_t n_seq_max);
+
+ ~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 shift) override;
+ void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
+
+ llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+ llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+ //
+ // llama_kv_cache
+ //
+
+ llama_memory_state_ptr init_batch(
+ const llama_batch & batch,
+ uint32_t n_ubatch,
+ bool embd_pooled,
+ bool logits_all) override;
+
+ llama_memory_state_ptr init_full() override;
+
+ bool update(llama_context & lctx) override;
+
+ void defrag_sched(float thold) override;
+
+ bool prepare(const std::vector<llama_ubatch> & ubatches);
+
+ // find a contiguous slot of kv cells and emplace the ubatch there
+ bool find_slot(const llama_ubatch & ubatch);
+
+ 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;
+
+ uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
+ uint32_t size = 0; // total number of cells, shared across all sequences
+ uint32_t used = 0; // used cells (i.e. at least one seq_id)
+
+ // computed before each graph build
+ uint32_t n = 0;
+
+ // TODO: optimize for recurrent state needs
+ 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;
+ }
+ };
+
+ 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;
+
+ const uint32_t n_seq_max = 1;
+
+ std::vector<ggml_context_ptr> ctxs;
+ std::vector<ggml_backend_buffer_ptr> bufs;
+
+ 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_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);
+};
+
+class llama_kv_cache_recurrent_state : public llama_memory_state_i {
+public:
+ // used for errors
+ llama_kv_cache_recurrent_state(llama_memory_status status);
+
+ // used to create a full-cache state
+ llama_kv_cache_recurrent_state(
+ llama_memory_status status,
+ llama_kv_cache_recurrent * kv);
+
+ // used to create a state from a batch
+ llama_kv_cache_recurrent_state(
+ llama_memory_status status,
+ llama_kv_cache_recurrent * kv,
+ llama_sbatch sbatch,
+ std::vector<llama_ubatch> ubatches);
+
+ virtual ~llama_kv_cache_recurrent_state();
+
+ //
+ // llama_memory_state_i
+ //
+
+ bool next() override;
+ bool apply() override;
+
+ std::vector<int64_t> & out_ids() override;
+
+ llama_memory_status get_status() const override;
+ const llama_ubatch & get_ubatch() const override;
+
+ //
+ // llama_kv_cache_recurrent_state specific API
+ //
+
+ uint32_t get_n_kv() const;
+ uint32_t get_head() const;
+ uint32_t get_size() const;
+
+ ggml_tensor * get_k_l(int32_t il) const;
+ ggml_tensor * get_v_l(int32_t il) const;
+
+ int32_t s_copy(int i) const;
+ float s_mask(int i) const;
+
+private:
+ const llama_memory_status status;
+
+ llama_kv_cache_recurrent * kv;
+
+ llama_sbatch sbatch;
+
+ size_t i_next = 0;
+
+ std::vector<llama_ubatch> ubatches;
+
+ //
+ // data needed for building the compute graph for the current ubatch:
+ // TODO: extract all the state like `head` and `n` here
+ //
+
+ const bool is_full = false;
+};
--- /dev/null
+#include "llama-kv-cache-unified-iswa.h"
+
+#include "llama-impl.h"
+#include "llama-batch.h"
+#include "llama-model.h"
+
+#include <algorithm>
+#include <cassert>
+
+//
+// llama_kv_cache_unified_iswa
+//
+
+llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
+ const llama_model & model,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool v_trans,
+ bool offload,
+ bool swa_full,
+ uint32_t kv_size,
+ uint32_t n_seq_max,
+ uint32_t n_ubatch,
+ uint32_t n_pad) : hparams(model.hparams) {
+ llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
+ llama_kv_cache_unified::layer_filter_cb filter_swa = [&](int32_t il) { return model.hparams.is_swa(il); };
+
+ const uint32_t size_base = kv_size;
+
+ uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_ubatch, n_pad));
+
+ // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size
+ if (swa_full) {
+ LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n",
+ __func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
+
+ size_swa = size_base;
+ }
+
+ LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
+
+ kv_base = std::make_unique<llama_kv_cache_unified>(
+ model, std::move(filter_base), type_k, type_v,
+ v_trans, offload, size_base, n_seq_max, n_pad,
+ 0, LLAMA_SWA_TYPE_NONE);
+
+ LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
+
+ kv_swa = std::make_unique<llama_kv_cache_unified>(
+ model, std::move(filter_swa), type_k, type_v,
+ v_trans, offload, size_swa, n_seq_max, n_pad,
+ hparams.n_swa, hparams.swa_type);
+}
+
+void llama_kv_cache_unified_iswa::clear() {
+ kv_base->clear();
+ kv_swa ->clear();
+}
+
+bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+ bool res = true;
+
+ res = res & kv_base->seq_rm(seq_id, p0, p1);
+ res = res & kv_swa ->seq_rm(seq_id, p0, p1);
+
+ return res;
+}
+
+void llama_kv_cache_unified_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+ kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+ kv_swa ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+}
+
+void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) {
+ kv_base->seq_keep(seq_id);
+ kv_swa ->seq_keep(seq_id);
+}
+
+void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+ kv_base->seq_add(seq_id, p0, p1, shift);
+ kv_swa ->seq_add(seq_id, p0, p1, shift);
+}
+
+void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+ kv_base->seq_div(seq_id, p0, p1, d);
+ kv_swa ->seq_div(seq_id, p0, p1, d);
+}
+
+llama_pos llama_kv_cache_unified_iswa::seq_pos_min(llama_seq_id seq_id) const {
+ // the base cache is a superset of the SWA cache, so we can just check the SWA cache
+ return kv_swa->seq_pos_min(seq_id);
+}
+
+llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
+ return kv_swa->seq_pos_max(seq_id);
+}
+
+llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) {
+ GGML_UNUSED(embd_pooled);
+
+ // TODO: if we fail with split_simple, we should attempt different splitting strategies
+ // but to do that properly, we first have to refactor the batches to be more flexible
+
+ auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
+
+ std::vector<llama_ubatch> ubatches;
+
+ while (sbatch.n_tokens > 0) {
+ auto ubatch = sbatch.split_simple(n_ubatch);
+
+ ubatches.push_back(ubatch);
+ }
+
+ auto heads_base = kv_base->prepare(ubatches);
+ if (heads_base.empty()) {
+ return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ }
+
+ auto heads_swa = kv_swa->prepare(ubatches);
+ if (heads_swa.empty()) {
+ return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ }
+
+ assert(heads_base.size() == heads_swa.size());
+
+ return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_SUCCESS,
+ this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches));
+}
+
+llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {
+ return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_SUCCESS, this);
+}
+
+bool llama_kv_cache_unified_iswa::update(llama_context & lctx) {
+ bool res = false;
+
+ res = res | kv_base->update(lctx);
+ res = res | kv_swa ->update(lctx);
+
+ return res;
+}
+
+void llama_kv_cache_unified_iswa::defrag_sched(float thold) {
+ kv_base->defrag_sched(thold);
+ kv_swa ->defrag_sched(thold);
+}
+
+bool llama_kv_cache_unified_iswa::get_can_shift() const {
+ return kv_base->get_size() == kv_swa->get_size();
+}
+
+void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
+ kv_base->state_write(io, seq_id);
+ kv_swa ->state_write(io, seq_id);
+}
+
+void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
+ kv_base->state_read(io, seq_id);
+ kv_swa ->state_read(io, seq_id);
+}
+
+llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_base() const {
+ return kv_base.get();
+}
+
+llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const {
+ return kv_swa.get();
+}
+
+//
+// llama_kv_cache_unified_iswa_state
+//
+
+llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {}
+
+llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
+ llama_memory_status status,
+ llama_kv_cache_unified_iswa * kv) : status(status) {
+ state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base()));
+ state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa ()));
+}
+
+llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
+ llama_memory_status status,
+ llama_kv_cache_unified_iswa * kv,
+ llama_sbatch sbatch,
+ std::vector<uint32_t> heads_base,
+ std::vector<uint32_t> heads_swa,
+ std::vector<llama_ubatch> ubatches)
+ : status(status),
+ sbatch(std::move(sbatch)),
+ ubatches(std::move(ubatches)) {
+ // note: here we copy the ubatches. not sure if this is ideal
+ state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base(), {}, std::move(heads_base), this->ubatches));
+ state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa (), {}, std::move(heads_swa), this->ubatches));
+ }
+
+llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default;
+
+bool llama_kv_cache_unified_iswa_state::next() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ state_base->next();
+ state_swa ->next();
+
+ if (++i_next >= ubatches.size()) {
+ return false;
+ }
+
+ return true;
+}
+
+bool llama_kv_cache_unified_iswa_state::apply() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ bool res = true;
+
+ res = res & state_base->apply();
+ res = res & state_swa ->apply();
+
+ return res;
+}
+
+std::vector<int64_t> & llama_kv_cache_unified_iswa_state::out_ids() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return sbatch.out_ids;
+}
+
+llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const {
+ return status;
+}
+
+const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+ return ubatches[i_next];
+}
+
+const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return state_base.get();
+}
+
+const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return state_swa.get();
+}
--- /dev/null
+#pragma once
+
+#include "llama-kv-cache-unified.h"
+
+#include <vector>
+
+//
+// llama_kv_cache_unified_iswa
+//
+
+// utilizes two instances of llama_kv_cache_unified
+// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
+
+class llama_kv_cache_unified_iswa : public llama_kv_cache {
+public:
+ llama_kv_cache_unified_iswa(
+ const llama_model & model,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool v_trans,
+ bool offload,
+ bool swa_full,
+ uint32_t kv_size,
+ uint32_t n_seq_max,
+ uint32_t n_ubatch,
+ uint32_t n_pad);
+
+ ~llama_kv_cache_unified_iswa() = 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 shift) override;
+ void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
+
+ llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+ llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+ //
+ // llama_kv_cache
+ //
+
+ llama_memory_state_ptr init_batch(
+ const llama_batch & batch,
+ uint32_t n_ubatch,
+ bool embd_pooled,
+ bool logits_all) override;
+
+ llama_memory_state_ptr init_full() override;
+
+ bool update(llama_context & lctx) override;
+
+ void defrag_sched(float thold) override;
+
+ bool get_can_shift() const override;
+
+ // 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;
+
+ //
+ // llama_kv_cache_unified_iswa specific API
+ //
+
+ llama_kv_cache_unified * get_base() const;
+ llama_kv_cache_unified * get_swa () const;
+
+private:
+ const llama_hparams & hparams;
+
+ std::unique_ptr<llama_kv_cache_unified> kv_base;
+ std::unique_ptr<llama_kv_cache_unified> kv_swa;
+};
+
+class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
+public:
+ // used for errors
+ llama_kv_cache_unified_iswa_state(llama_memory_status status);
+
+ // used to create a full-cache state
+ llama_kv_cache_unified_iswa_state(
+ llama_memory_status status,
+ llama_kv_cache_unified_iswa * kv);
+
+ // used to create a state from a batch
+ llama_kv_cache_unified_iswa_state(
+ llama_memory_status status,
+ llama_kv_cache_unified_iswa * kv,
+ llama_sbatch sbatch,
+ std::vector<uint32_t> heads_base,
+ std::vector<uint32_t> heads_swa,
+ std::vector<llama_ubatch> ubatches);
+
+ virtual ~llama_kv_cache_unified_iswa_state();
+
+ //
+ // llama_memory_state_i
+ //
+
+ bool next() override;
+ bool apply() override;
+
+ std::vector<int64_t> & out_ids() override;
+
+ llama_memory_status get_status() const override;
+ const llama_ubatch & get_ubatch() const override;
+
+ //
+ // llama_kv_cache_unified_iswa_state specific API
+ //
+
+ const llama_kv_cache_unified_state * get_base() const;
+ const llama_kv_cache_unified_state * get_swa() const;
+
+private:
+ const llama_memory_status status;
+
+ //llama_kv_cache_unified_iswa * kv;
+
+ llama_sbatch sbatch;
+
+ // the index of the next ubatch to process
+ size_t i_next = 0;
+
+ std::vector<llama_ubatch> ubatches;
+
+ std::unique_ptr<llama_kv_cache_unified_state> state_base;
+ std::unique_ptr<llama_kv_cache_unified_state> state_swa;
+};
--- /dev/null
+#include "llama-kv-cache-unified.h"
+
+#include "llama-impl.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::llama_kv_cache_unified(
+ const llama_model & model,
+ layer_filter_cb && filter,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool v_trans,
+ bool offload,
+ uint32_t kv_size,
+ uint32_t n_seq_max,
+ uint32_t n_pad,
+ uint32_t n_swa,
+ llama_swa_type swa_type) :
+ model(model), hparams(model.hparams), v_trans(v_trans),
+ n_seq_max(n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
+
+ GGML_ASSERT(kv_size % n_pad == 0);
+
+ // create a context for each buffer type
+ std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+ 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*hparams.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;
+ };
+
+ head = 0;
+
+ cells.resize(kv_size);
+
+ for (uint32_t il = 0; il < hparams.n_layer; il++) {
+ if (filter && !filter(il)) {
+ LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
+ continue;
+ }
+
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + 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(il);
+ buft = ggml_backend_dev_buffer_type(dev);
+
+ dev_name = ggml_backend_dev_name(dev);
+ }
+
+ LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, il, 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_tensor * v;
+
+ k = ggml_new_tensor_2d(ctx, type_k, n_embd_k_gqa, kv_size);
+ v = ggml_new_tensor_2d(ctx, type_v, n_embd_v_gqa, kv_size);
+
+ ggml_format_name(k, "cache_k_l%d", il);
+ ggml_format_name(v, "cache_v_l%d", il);
+
+ map_layer_ids[il] = layers.size();
+ layers.push_back({ il, k, 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");
+ }
+
+ 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);
+
+ ggml_backend_buffer_clear(buf, 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: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
+ (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max,
+ ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
+ ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
+ }
+}
+
+void llama_kv_cache_unified::clear() {
+ cells.reset();
+
+ head = 0;
+
+ for (auto & buf : bufs) {
+ ggml_backend_buffer_clear(buf.get(), 0);
+ }
+}
+
+bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+ uint32_t new_head = cells.size();
+
+ if (p0 < 0) {
+ p0 = 0;
+ }
+
+ if (p1 < 0) {
+ p1 = std::numeric_limits<llama_pos>::max();
+ }
+
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ if (!cells.pos_in(i, p0, p1)) {
+ continue;
+ }
+
+ if (cells.seq_has(i, seq_id) && cells.seq_rm(i, seq_id)) {
+ if (new_head == cells.size()) {
+ new_head = i;
+ }
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ if (new_head != cells.size() && new_head < head) {
+ head = new_head;
+ }
+
+ return true;
+}
+
+void llama_kv_cache_unified::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();
+ }
+
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ if (!cells.pos_in(i, p0, p1)) {
+ continue;
+ }
+
+ if (cells.seq_has(i, seq_id_src)) {
+ cells.seq_add(i, seq_id_dst);
+ }
+ }
+}
+
+void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) {
+ uint32_t new_head = cells.size();
+
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ if (cells.seq_keep(i, seq_id)) {
+ if (new_head == cells.size()) {
+ new_head = i;
+ }
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ if (new_head != cells.size() && new_head < head) {
+ head = new_head;
+ }
+}
+
+void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+ if (shift == 0) {
+ return;
+ }
+
+ uint32_t new_head = cells.size();
+
+ 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 all cells.
+ if (p0 == p1) {
+ return;
+ }
+
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ if (!cells.pos_in(i, p0, p1)) {
+ continue;
+ }
+
+ if (cells.seq_has(i, seq_id)) {
+ if (cells.pos_add(i, shift)) {
+ if (new_head == cells.size()) {
+ new_head = i;
+ }
+ }
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ // Otherwise we just start the next search from the beginning.
+ head = new_head != cells.size() ? new_head : 0;
+}
+
+void llama_kv_cache_unified::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 (uint32_t i = 0; i < cells.size(); ++i) {
+ if (!cells.pos_in(i, p0, p1)) {
+ continue;
+ }
+
+ if (cells.seq_has(i, seq_id)) {
+ cells.pos_div(i, d);
+ }
+ }
+}
+
+llama_pos llama_kv_cache_unified::seq_pos_min(llama_seq_id seq_id) const {
+ return cells.seq_pos_min(seq_id);
+}
+
+llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
+ return cells.seq_pos_max(seq_id);
+}
+
+llama_memory_state_ptr llama_kv_cache_unified::init_batch(
+ const llama_batch & batch,
+ uint32_t n_ubatch,
+ bool embd_pooled,
+ bool logits_all) {
+ GGML_UNUSED(embd_pooled);
+
+ auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all);
+
+ std::vector<llama_ubatch> ubatches;
+ while (sbatch.n_tokens > 0) {
+ ubatches.push_back(sbatch.split_simple(n_ubatch));
+ }
+
+ auto heads = prepare(ubatches);
+ if (heads.empty()) {
+ return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+ }
+
+ return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_SUCCESS,
+ this, std::move(sbatch), std::move(heads), std::move(ubatches));
+}
+
+llama_memory_state_ptr llama_kv_cache_unified::init_full() {
+ return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_SUCCESS, this);
+}
+
+std::vector<uint32_t> llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
+ std::vector<uint32_t> res;
+
+ struct state {
+ uint32_t head_old; // old position of the head, before placing the ubatch
+ uint32_t head_new; // new position of the head, after placing the ubatch
+
+ llama_kv_cells_unified cells; // copy of the old cells, before placing the ubatch
+ };
+
+ // remember the old state of the cells so we can restore it in the end
+ std::vector<state> states;
+
+ bool success = true;
+
+ for (const auto & ubatch : ubatches) {
+ // only find a suitable slot for the ubatch. don't modify the cells yet
+ const int32_t head_new = find_slot(ubatch);
+ if (head_new < 0) {
+ success = false;
+ break;
+ }
+
+ // remeber the position that we found
+ res.push_back(head_new);
+
+ // store the old state of the cells in the recovery stack
+ states.push_back({head, (uint32_t) head_new, cells.cp(head_new, ubatch.n_tokens)});
+
+ // now emplace the ubatch
+ apply_ubatch(head_new, ubatch);
+ }
+
+ // iterate backwards and restore the cells to their original state
+ for (auto it = states.rbegin(); it != states.rend(); ++it) {
+ cells.set(it->head_new, it->cells);
+ head = it->head_old;
+ }
+
+ if (!success) {
+ return {};
+ }
+
+ return res;
+}
+
+bool llama_kv_cache_unified::update(llama_context & lctx) {
+ bool updated = false;
+
+ auto * sched = lctx.get_sched();
+
+ if (cells.get_has_shift()) {
+ if (!get_can_shift()) {
+ GGML_ABORT("The current KV cache / model configuration does not support K-shift");
+ }
+
+ LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__);
+
+ // apply K-shift if needed
+ if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
+ ggml_backend_sched_reset(sched);
+
+ auto * gf = lctx.graph_init();
+
+ auto res = build_graph_shift(lctx.get_cparams(), lctx.get_ctx_compute(), gf);
+ if (!res) {
+ LLAMA_LOG_ERROR("%s: failed to build graph for K-shift\n", __func__);
+ return updated;
+ }
+
+ if (!ggml_backend_sched_alloc_graph(sched, gf)) {
+ LLAMA_LOG_ERROR("%s: failed to allocate compute graph for K-shift\n", __func__);
+ return updated;
+ }
+
+ res->set_inputs(nullptr);
+
+ if (lctx.graph_compute(gf, false) != GGML_STATUS_SUCCESS) {
+ LLAMA_LOG_ERROR("%s: failed to compute K-shift\n", __func__);
+ return updated;
+ }
+
+ updated = true;
+ }
+
+ cells.reset_shift();
+ }
+
+ if (do_defrag) {
+ LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
+
+ if (defrag_prepare(lctx.graph_max_nodes())) {
+ ggml_backend_sched_reset(sched);
+
+ auto * gf = lctx.graph_init();
+
+ auto res = build_graph_defrag(lctx.get_cparams(), lctx.get_ctx_compute(), gf);
+ if (!res) {
+ LLAMA_LOG_ERROR("%s: failed to build graph for defrag\n", __func__);
+ return updated;
+ }
+
+ if (!ggml_backend_sched_alloc_graph(sched, gf)) {
+ LLAMA_LOG_ERROR("%s: failed to allocate compute graph for defrag\n", __func__);
+ return updated;
+ }
+
+ res->set_inputs(nullptr);
+
+ if (lctx.graph_compute(gf, false) != GGML_STATUS_SUCCESS) {
+ LLAMA_LOG_ERROR("%s: failed to compute defrag\n", __func__);
+ return updated;
+ }
+
+ updated = true;
+ }
+
+ do_defrag = false;
+ }
+
+ return updated;
+}
+
+void llama_kv_cache_unified::defrag_sched(float thold) {
+ const auto n_kv = cells.used_max_p1();
+
+ // - do not defrag small contexts (i.e. < 2048 tokens)
+ // - count the padding towards the number of used tokens
+ const float fragmentation = n_kv >= 2048 ? std::max(0.0f, 1.0f - (float(cells.get_used() + n_pad)/n_kv)) : 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;
+ }
+}
+
+int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
+ const uint32_t n_tokens = ubatch.n_tokens;
+
+ uint32_t head_cur = this->head;
+
+ // 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_cur > cells.get_used() + 2*ubatch.n_tokens) {
+ head_cur = 0;
+ }
+
+ // otherwise, one cell per token.
+
+ if (n_tokens > cells.size()) {
+ LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
+ return -1;
+ }
+
+//#define FIND_SLOT_DEBUG 1
+#if FIND_SLOT_DEBUG
+ LLAMA_LOG_WARN("begin: n = %5d, used = %5d, head = %5d, n_swa = %5d\n", cells.used_max_p1(), cells.get_used(), head, n_swa);
+
+ // for debugging
+ {
+ std::string ss;
+ if (n_swa > 0) {
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ if (cells.is_empty(i)) {
+ ss += '.';
+ } else {
+ ss += std::to_string(cells.seq_get(i));
+ }
+ if (i%256 == 255) {
+ ss += '\n';
+ }
+ }
+ }
+ LLAMA_LOG_WARN("\n%s\n", ss.c_str());
+ }
+
+ for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+ if (cells.seq_pos_min(s) < 0) {
+ continue;
+ }
+
+ LLAMA_LOG_WARN("kv_cells: n_swa = %4d, min[%d] = %5d, max[%d] = %5d\n", n_swa, s, cells.seq_pos_min(s), s, cells.seq_pos_max(s));
+ }
+#endif
+
+ uint32_t n_tested = 0;
+
+ while (true) {
+ if (head_cur + n_tokens > cells.size()) {
+ n_tested += cells.size() - head_cur;
+ head_cur = 0;
+ continue;
+ }
+
+ // keep track of what the minimum sequence positions would be if we accept the ubatch
+ llama_seq_id seq_pos_min[LLAMA_MAX_PARALLEL_SEQUENCES];
+ for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+ seq_pos_min[s] = cells.seq_pos_min(s);
+ }
+
+ bool found = true;
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ const llama_pos pos = ubatch.pos[i];
+ const llama_seq_id seq_id = ubatch.seq_id[i][0];
+
+ // can we use this cell? either:
+ // - the cell is empty
+ // - the cell is occupied only by one sequence:
+ // - mask causally, if the sequence is the same as the one we are inserting
+ // - mask SWA, using current max pos for that sequence in the cache
+ // always insert in the cell with minimum pos
+ bool can_use = cells.is_empty(head_cur + i);
+
+ if (!can_use && cells.seq_count(head_cur + i) == 1) {
+ const llama_pos pos_cell = cells.pos_get(head_cur + i);
+
+ // causal mask
+ if (cells.seq_has(head_cur + i, seq_id)) {
+ can_use = pos_cell >= pos;
+ }
+
+ if (!can_use) {
+ const llama_seq_id seq_id_cell = cells.seq_get(head_cur + i);
+
+ // SWA mask
+ // note: we insert only in the cell with minimum pos in order to preserve the invariant that
+ // all positions between [pos_min, pos_max] for each sequence will be present in the cache
+ // ref: https://github.com/ggml-org/llama.cpp/pull/13746#issuecomment-2916057092
+ if (pos_cell == seq_pos_min[seq_id_cell] &&
+ is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
+ seq_pos_min[seq_id_cell]++;
+ can_use = true;
+ }
+ }
+ }
+
+ if (!can_use) {
+ found = false;
+ head_cur += i + 1;
+ n_tested += i + 1;
+ break;
+ }
+ }
+
+ if (found) {
+ break;
+ }
+
+ if (n_tested >= cells.size()) {
+ //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
+ return -1;
+ }
+ }
+
+ return head_cur;
+}
+
+void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) {
+ for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+ if (!cells.is_empty(head_cur + i)) {
+ cells.rm(head_cur + i);
+ }
+
+ cells.pos_set(head_cur + i, ubatch.pos[i]);
+
+ for (int32_t j = 0; j < ubatch.n_seq_id[i]; j++) {
+ cells.seq_add(head_cur + i, ubatch.seq_id[i][j]);
+ }
+ }
+
+ // move the head at the end of the slot
+ head = head_cur + ubatch.n_tokens;
+}
+
+bool llama_kv_cache_unified::get_can_shift() const {
+ return true;
+}
+
+uint32_t llama_kv_cache_unified::get_size() const {
+ return cells.size();
+}
+
+uint32_t llama_kv_cache_unified::get_n_kv() const {
+ return std::min(cells.size(), std::max(n_pad, GGML_PAD(cells.used_max_p1(), n_pad)));
+}
+
+ggml_tensor * llama_kv_cache_unified::get_k(ggml_context * ctx, int32_t il, uint32_t n_kv) const {
+ const int32_t ikv = map_layer_ids.at(il);
+
+ auto * k = layers[ikv].k;
+
+ return ggml_view_3d(ctx, k,
+ hparams.n_embd_head_k, hparams.n_head_kv(il), n_kv,
+ ggml_row_size(k->type, hparams.n_embd_head_k),
+ ggml_row_size(k->type, hparams.n_embd_k_gqa(il)),
+ 0);
+}
+
+ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const {
+ const int32_t ikv = map_layer_ids.at(il);
+
+ auto * v = layers[ikv].v;
+
+ if (!v_trans) {
+ // note: v->nb[1] <= v->nb[2]
+ return ggml_view_3d(ctx, v,
+ hparams.n_embd_head_v, hparams.n_head_kv(il), n_kv,
+ ggml_row_size(v->type, hparams.n_embd_head_v), // v->nb[1]
+ ggml_row_size(v->type, hparams.n_embd_v_gqa(il)), // v->nb[2]
+ 0);
+ }
+
+ // note: v->nb[1] > v->nb[2]
+ return ggml_view_3d(ctx, v,
+ n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v,
+ ggml_row_size(v->type, v->ne[1]*hparams.n_embd_head_v), // v->nb[1]
+ ggml_row_size(v->type, v->ne[1]), // v->nb[2]
+ 0);
+}
+
+ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const {
+ const int32_t ikv = map_layer_ids.at(il);
+
+ auto * k = layers[ikv].k;
+
+ const int64_t n_tokens = k_cur->ne[2];
+
+ ggml_tensor * k_view = ggml_view_1d(ctx, k,
+ n_tokens*hparams.n_embd_k_gqa(il),
+ ggml_row_size(k->type, hparams.n_embd_k_gqa(il))*head_cur);
+
+ return ggml_cpy(ctx, k_cur, k_view);
+}
+
+ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const {
+ const int32_t ikv = map_layer_ids.at(il);
+
+ auto * v = layers[ikv].v;
+
+ const int64_t n_tokens = v_cur->ne[2];
+
+ v_cur = ggml_reshape_2d(ctx, v_cur, hparams.n_embd_v_gqa(il), n_tokens);
+
+ ggml_tensor * v_view = nullptr;
+
+ if (!v_trans) {
+ v_view = ggml_view_1d(ctx, v,
+ n_tokens*hparams.n_embd_v_gqa(il),
+ ggml_row_size(v->type, hparams.n_embd_v_gqa(il))*head_cur);
+ } else {
+ // note: the V cache is transposed when not using flash attention
+ v_view = ggml_view_2d(ctx, v, n_tokens, hparams.n_embd_v_gqa(il),
+ (v->ne[1])*ggml_element_size(v),
+ (head_cur)*ggml_element_size(v));
+
+ v_cur = ggml_transpose(ctx, v_cur);
+ }
+
+ return ggml_cpy(ctx, v_cur, v_view);
+}
+
+void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
+ const int64_t n_tokens = ubatch->n_tokens;
+ const int64_t n_seq_tokens = ubatch->n_seq_tokens;
+ const int64_t n_seqs = ubatch->n_seqs;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
+ float * data = (float *) dst->data;
+
+ const auto n_kv = dst->ne[0];
+
+ // Use only the previous KV cells of the correct sequence for each token of the ubatch.
+ // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
+ // Example with a cache of 10 tokens, 2 tokens populated in cache and 3 tokens in batch:
+ // Causal mask:
+ // xxx-------
+ // xxxx------
+ // xxxxx-----
+ // Non-causal mask:
+ // xxxxx-----
+ // xxxxx-----
+ // xxxxx-----
+ // To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
+ for (int h = 0; h < 1; ++h) {
+ for (int s = 0; s < n_seqs; ++s) {
+ const llama_seq_id seq_id = ubatch->seq_id[s][0];
+
+ for (int j = 0; j < n_seq_tokens; ++j) {
+ const llama_pos p1 = ubatch->pos[s*n_seq_tokens + j];
+
+ for (uint32_t i = 0; i < n_kv; ++i) {
+ float f = 0.0f;
+
+ bool masked = false;
+
+ if (cells.is_empty(i)) {
+ masked = true;
+ } else {
+ const llama_pos p0 = cells.pos_get(i);
+
+ // mask the token if not the same sequence
+ masked = masked || (!cells.seq_has(i, seq_id));
+
+ // mask future tokens
+ masked = masked || (causal_attn && p0 > p1);
+
+ // apply SWA if any
+ masked = masked || (is_masked_swa(p0, p1));
+
+ if (!masked && hparams.use_alibi) {
+ f = -std::abs(p0 - p1);
+ }
+ }
+
+ if (masked) {
+ f = -INFINITY;
+ }
+
+ data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+ }
+ }
+ }
+
+ // mask padded tokens
+ if (data) {
+ for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+ for (uint32_t j = 0; j < n_kv; ++j) {
+ data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+ }
+ }
+ }
+ }
+}
+
+void llama_kv_cache_unified::set_input_k_shift(ggml_tensor * dst) const {
+ GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
+
+ int32_t * data = (int32_t *) dst->data;
+
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ data[i] = cells.is_empty(i) ? 0 : cells.get_shift(i);
+ }
+}
+
+void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+ const int64_t n_tokens = ubatch->n_tokens;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
+ GGML_ASSERT(!ubatch->equal_seqs); // TODO: use ubatch->n_seqs instead of failing
+
+ int32_t * data = (int32_t *) dst->data;
+
+ const int32_t n_kv = dst->ne[0];
+
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ for (int i = 0; i < n_kv; ++i) {
+ // the position when the cells is empty is irrelevant - it will be masked out later in the attention
+ const llama_pos p0 = cells.is_empty(i) ? -1 : cells.pos_get(i);
+
+ data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(p0, ubatch->pos[j], hparams.n_rel_attn_bkts, false);
+ }
+ }
+ }
+}
+
+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 {
+ size_t size_k_bytes = 0;
+
+ for (const auto & layer : layers) {
+ size_k_bytes += ggml_nbytes(layer.k);
+ }
+
+ return size_k_bytes;
+}
+
+size_t llama_kv_cache_unified::size_v_bytes() const {
+ size_t size_v_bytes = 0;
+
+ for (const auto & layer : layers) {
+ size_v_bytes += ggml_nbytes(layer.v);
+ }
+
+ return size_v_bytes;
+}
+
+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 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 & n_rot = hparams.n_rot;
+ const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE
+ // @ngxson : this is a workaround
+ // for M-RoPE, we want to rotate the whole vector when doing KV shift
+ // a normal RoPE should work, we just need to use the correct ordering
+ // ref: https://github.com/ggml-org/llama.cpp/pull/13870
+ ? LLAMA_ROPE_TYPE_NEOX
+ : 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(ctx, cur, GGML_TYPE_F32);
+
+ 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);
+
+ 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);
+ }
+
+ 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) {
+ kv_self->set_input_k_shift(k_shift);
+ }
+}
+
+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>();
+
+ 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>(this);
+
+ inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, cparams.n_ctx);
+ ggml_set_input(inp->k_shift);
+
+ for (const auto & layer : layers) {
+ const uint32_t il = 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 float freq_base_l = model.get_rope_freq_base (cparams, il);
+ const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+ ggml_tensor * k =
+ ggml_view_3d(ctx, layer.k,
+ n_embd_head_k, n_head_kv, cells.size(),
+ ggml_row_size(layer.k->type, n_embd_head_k),
+ ggml_row_size(layer.k->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 (const auto & layer : layers) {
+ const uint32_t il = layer.il;
+
+ 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, layer.k,
+ n_embd_k_gqa, nm,
+ ggml_row_size(layer.k->type, n_embd_k_gqa),
+ ggml_row_size(layer.k->type, n_embd_k_gqa*i));
+
+ ggml_tensor * view_k_dst = ggml_view_2d(ctx, layer.k,
+ n_embd_k_gqa, nm,
+ ggml_row_size(layer.k->type, n_embd_k_gqa),
+ ggml_row_size(layer.k->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, layer.v,
+ n_embd_v_gqa, nm,
+ ggml_row_size(layer.v->type, n_embd_v_gqa),
+ ggml_row_size(layer.v->type, n_embd_v_gqa*i));
+
+ view_v_dst = ggml_view_2d(ctx, layer.v,
+ n_embd_v_gqa, nm,
+ ggml_row_size(layer.v->type, n_embd_v_gqa),
+ ggml_row_size(layer.v->type, n_embd_v_gqa*id));
+ } else {
+ view_v_src = ggml_view_2d(ctx, layer.v,
+ nm, n_embd_v_gqa,
+ ggml_row_size(layer.v->type, cells.size()),
+ ggml_row_size(layer.v->type, i));
+
+ view_v_dst = ggml_view_2d(ctx, layer.v,
+ nm, n_embd_v_gqa,
+ ggml_row_size(layer.v->type, cells.size()),
+ ggml_row_size(layer.v->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 = layers.size();
+
+ const uint32_t n_kv = cells.used_max_p1();
+ const uint32_t n_used = cells.get_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) {
+ if (!cells.is_empty(i0)) {
+ 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.is_empty(i0 + nh)) {
+ nh++;
+ }
+
+ uint32_t nf = 0;
+ uint32_t is = n_kv - 1;
+
+ // starting from the end, find nh non-empty cells
+ for (; is > i0; --is) {
+ if (cells.is_empty(is) || ids[is] != n_kv) {
+ continue;
+ }
+
+ // non-empty cell which is not yet moved
+ nf++;
+
+ if (nf == nh) {
+ break;
+ }
+ }
+
+ // this can only happen if `n_used` is not accurate, which would be a bug
+ GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
+
+ nf = 0;
+
+ uint32_t i1 = is;
+
+ // are we moving a continuous block of memory?
+ bool cont = false;
+
+ // should we stop searching for the next move?
+ bool stop = false;
+
+ // go back and move the nf cells to the hole
+ for (; i1 < n_kv; ++i1) {
+ if (cells.is_empty(i1) || ids[i1] != n_kv) {
+ if (n_moves == max_moves) {
+ stop = true;
+ break;
+ }
+
+ cont = false;
+ continue;
+ }
+
+ // this cell goes to (i0 + nf)
+ ids[i1] = i0 + nf;
+
+ // move the cell meta data
+ cells.mv(i1, i0 + nf);
+
+ head = n_used;
+
+ if (!cont) {
+ n_moves++;
+ cont = true;
+ }
+
+ nf++;
+
+ if (nf == nh) {
+ break;
+ }
+ }
+
+ if (stop || n_moves == max_moves) {
+ break;
+ }
+
+ //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
+
+ i0 += nh - 1;
+ }
+
+ if (n_moves == 0) {
+ return false;
+ }
+
+ LLAMA_LOG_DEBUG("%s: (tmp log) KV defrag cell moves: %u\n", __func__, n_moves);
+
+ LLAMA_LOG_DEBUG("%s: expected gf nodes: %u\n", __func__, 6*n_moves*n_layer);
+
+ return true;
+}
+
+bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
+ assert(p0 >= 0 && p1 >= 0);
+
+ switch (swa_type) {
+ case LLAMA_SWA_TYPE_NONE:
+ {
+ } break;
+ case LLAMA_SWA_TYPE_STANDARD:
+ {
+ if (p1 - p0 >= (int32_t) n_swa) {
+ return true;
+ }
+ } break;
+ case LLAMA_SWA_TYPE_CHUNKED:
+ {
+ const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
+
+ if (p0 < pos_chunk_start) {
+ return true;
+ }
+ } break;
+ }
+
+ return false;
+}
+
+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;
+
+ // 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 = cells.size();
+
+ for (uint32_t i = 0; i < cells.size(); ++i) {
+ if (!cells.is_empty(i) && (seq_id == -1 || cells.seq_has(i, seq_id))) {
+ ++cell_count;
+ if (cell_range_begin == cells.size()) {
+ cell_range_begin = i;
+ }
+ } else {
+ if (cell_range_begin != cells.size()) {
+ cell_ranges.emplace_back(cell_range_begin, i);
+ cell_range_begin = cells.size();
+ }
+ }
+ }
+
+ if (cell_range_begin != cells.size()) {
+ cell_ranges.emplace_back(cell_range_begin, cells.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_unified::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_unified::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) {
+ std::vector<llama_seq_id> seq_ids;
+
+ for (llama_seq_id cur = 0; cur < (int) n_seq_max; ++cur) {
+ if (cur == seq_id || seq_id == -1) {
+ if (cells.seq_has(i, cur)) {
+ seq_ids.push_back(cur);
+ }
+ }
+ }
+
+ const llama_pos pos = cells.pos_get(i);
+ const uint32_t n_seq_id = seq_ids.size();
+
+ io.write(&pos, sizeof(pos));
+ io.write(&n_seq_id, sizeof(n_seq_id));
+
+ for (const auto & seq_id : seq_ids) {
+ io.write(&seq_id, sizeof(seq_id));
+ }
+ }
+ }
+}
+
+void llama_kv_cache_unified::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 = this->v_trans ? 1 : 0;
+ const uint32_t n_layer = layers.size();
+
+ 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 (const auto & layer : layers) {
+ const uint32_t il = 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)layer.k->type;
+ io.write(&k_type_i, sizeof(k_type_i));
+
+ // Write row size of key
+ const uint64_t k_size_row = ggml_row_size(layer.k->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(layer.k, range.first * k_size_row, buf_size);
+ }
+ }
+
+ if (!v_trans) {
+ for (const auto & layer : layers) {
+ const uint32_t il = 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)layer.v->type;
+ io.write(&v_type_i, sizeof(v_type_i));
+
+ // Write row size of value
+ const uint64_t v_size_row = ggml_row_size(layer.v->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(layer.v, 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 = cells.size();
+
+ for (const auto & layer : layers) {
+ const uint32_t il = 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)layer.v->type;
+ io.write(&v_type_i, sizeof(v_type_i));
+
+ // Write element size
+ const uint32_t v_size_el = ggml_type_size(layer.v->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(layer.v, src_offset, buf_size);
+ }
+ }
+ }
+ }
+}
+
+bool llama_kv_cache_unified::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;
+
+ 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 != 1) {
+ LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
+ return false;
+ }
+
+ // read the sequence id, but directly discard it - we will use dest_seq_id instead
+ {
+ llama_seq_id seq_id;
+ io.read_to(&seq_id, sizeof(seq_id));
+ }
+
+ batch.pos[i] = pos;
+ batch.n_seq_id[i] = n_seq_id;
+ batch.seq_id[i] = &dest_seq_id;
+ }
+
+ const auto head_cur = find_slot(batch);
+ if (head_cur < 0) {
+ LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+ return false;
+ }
+
+ apply_ubatch(head_cur, batch);
+
+ // keep the head at the old position because we will read the KV data into it in state_read_data()
+ head = head_cur;
+
+ // DEBUG CHECK: head_cur should be our first cell, head_cur + 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_cur + cell_count <= cells.size());
+ GGML_ASSERT(cells.pos_get(head_cur) == batch.pos[0]);
+ GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == batch.pos[cell_count - 1]);
+ GGML_ASSERT(cells.seq_has(head_cur, dest_seq_id));
+ GGML_ASSERT(cells.seq_has(head_cur + cell_count - 1, dest_seq_id));
+ } else {
+ // whole KV cache restore
+
+ if (cell_count > cells.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) {
+ llama_pos pos;
+ uint32_t n_seq_id;
+
+ io.read_to(&pos, sizeof(pos));
+ io.read_to(&n_seq_id, sizeof(n_seq_id));
+
+ cells.pos_set(i, pos);
+
+ for (uint32_t j = 0; j < n_seq_id; ++j) {
+ llama_seq_id seq_id;
+ io.read_to(&seq_id, sizeof(seq_id));
+
+ if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
+ LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, n_seq_max);
+ return false;
+ }
+
+ cells.seq_add(i, seq_id);
+ }
+ }
+
+ head = 0;
+ }
+
+ return true;
+}
+
+bool llama_kv_cache_unified::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 != layers.size()) {
+ LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, (uint32_t) layers.size());
+ return false;
+ }
+
+ if (cell_count > cells.size()) {
+ LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, cells.size());
+ return false;
+ }
+
+ 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 (const auto & layer : layers) {
+ const uint32_t il = 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) layer.k->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(layer.k->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(layer.k, io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
+ }
+ }
+
+ if (!this->v_trans) {
+ for (const auto & layer : layers) {
+ const uint32_t il = 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)layer.v->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(layer.v->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(layer.v, 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 (const auto & layer : layers) {
+ const uint32_t il = 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)layer.v->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(layer.v->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 * cells.size()) * v_size_el;
+ ggml_backend_tensor_set(layer.v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+ }
+ }
+ }
+ }
+
+ return true;
+}
+
+//
+// llama_kv_cache_unified_state
+//
+
+llama_kv_cache_unified_state::llama_kv_cache_unified_state(llama_memory_status status) : status(status) {}
+
+llama_kv_cache_unified_state::llama_kv_cache_unified_state(
+ llama_memory_status status,
+ llama_kv_cache_unified * kv) : status(status), kv(kv) {
+ n_kv = kv->get_size();
+ head = 0;
+ }
+
+llama_kv_cache_unified_state::llama_kv_cache_unified_state(
+ llama_memory_status status,
+ llama_kv_cache_unified * kv,
+ llama_sbatch sbatch,
+ std::vector<uint32_t> heads,
+ std::vector<llama_ubatch> ubatches)
+ : status(status),
+ kv(kv),
+ sbatch(std::move(sbatch)),
+ heads(std::move(heads)),
+ ubatches(std::move(ubatches)) {
+ }
+
+llama_kv_cache_unified_state::~llama_kv_cache_unified_state() = default;
+
+bool llama_kv_cache_unified_state::next() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ if (++i_next >= ubatches.size()) {
+ return false;
+ }
+
+ return true;
+}
+
+bool llama_kv_cache_unified_state::apply() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ kv->apply_ubatch(heads[i_next], ubatches[i_next]);
+
+ n_kv = kv->get_n_kv();
+ head = heads[i_next];
+
+ return true;
+}
+
+std::vector<int64_t> & llama_kv_cache_unified_state::out_ids() {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return sbatch.out_ids;
+}
+
+llama_memory_status llama_kv_cache_unified_state::get_status() const {
+ return status;
+}
+
+const llama_ubatch & llama_kv_cache_unified_state::get_ubatch() const {
+ assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+ return ubatches[i_next];
+}
+
+uint32_t llama_kv_cache_unified_state::get_n_kv() const {
+ return n_kv;
+}
+
+ggml_tensor * llama_kv_cache_unified_state::get_k(ggml_context * ctx, int32_t il) const {
+ return kv->get_k(ctx, il, n_kv);
+}
+
+ggml_tensor * llama_kv_cache_unified_state::get_v(ggml_context * ctx, int32_t il) const {
+ return kv->get_v(ctx, il, n_kv);
+}
+
+ggml_tensor * llama_kv_cache_unified_state::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
+ return kv->cpy_k(ctx, k_cur, il, head);
+}
+
+ggml_tensor * llama_kv_cache_unified_state::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
+ return kv->cpy_v(ctx, v_cur, il, head);
+}
+
+void llama_kv_cache_unified_state::set_input_k_shift(ggml_tensor * dst) const {
+ kv->set_input_k_shift(dst);
+}
+
+void llama_kv_cache_unified_state::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
+ kv->set_input_kq_mask(dst, ubatch, causal_attn);
+}
+
+void llama_kv_cache_unified_state::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+ kv->set_input_pos_bucket(dst, ubatch);
+}
+
+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;
+}
--- /dev/null
+#pragma once
+
+#include "llama-batch.h"
+#include "llama-graph.h"
+#include "llama-kv-cache.h"
+#include "llama-kv-cells.h"
+
+#include <unordered_map>
+#include <vector>
+
+struct llama_cparams;
+struct llama_hparams;
+struct llama_model;
+struct llama_context;
+
+//
+// llama_kv_cache_unified
+//
+
+class llama_kv_cache_unified : public llama_kv_cache {
+public:
+ static uint32_t get_padding(const llama_cparams & cparams);
+
+ // this callback is used to filter out layers that should not be included in the cache
+ using layer_filter_cb = std::function<bool(int32_t il)>;
+
+ llama_kv_cache_unified(
+ const llama_model & model,
+ layer_filter_cb && filter,
+ ggml_type type_k,
+ ggml_type type_v,
+ bool v_trans,
+ bool offload,
+ uint32_t kv_size,
+ uint32_t n_seq_max,
+ uint32_t n_pad,
+ uint32_t n_swa,
+ llama_swa_type swa_type);
+
+ ~llama_kv_cache_unified() = 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 shift) override;
+ void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
+
+ llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+ llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+ //
+ // llama_kv_cache
+ //
+
+ llama_memory_state_ptr init_batch(
+ const llama_batch & batch,
+ uint32_t n_ubatch,
+ bool embd_pooled,
+ bool logits_all) override;
+
+ llama_memory_state_ptr init_full() override;
+
+ bool update(llama_context & lctx) override;
+
+ void defrag_sched(float thold) override;
+
+ bool get_can_shift() const override;
+
+ // 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;
+
+ //
+ // llama_kv_cache_unified specific API
+ //
+
+ uint32_t get_size() const;
+
+ //
+ // graph_build API
+ //
+
+ uint32_t get_n_kv() const;
+
+ // get views of the current state of the cache
+ ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
+ ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
+
+ // store k_cur and v_cur in the cache based on the provided head location
+ ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
+ ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
+
+ //
+ // preparation API
+ //
+
+ // find places for the provided ubatches in the cache, returns the head locations
+ // return empty vector on failure
+ std::vector<uint32_t> prepare(const std::vector<llama_ubatch> & ubatches);
+
+ // return the cell position where we can insert the ubatch
+ // return -1 on failure to find a contiguous slot of kv cells
+ int32_t find_slot(const llama_ubatch & ubatch) const;
+
+ // emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
+ void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
+
+ //
+ // set_input API
+ //
+
+ void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
+ void set_input_k_shift (ggml_tensor * dst) const;
+ void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
+
+private:
+ const llama_model & model;
+ const llama_hparams & hparams;
+
+ struct kv_layer {
+ // layer index in the model
+ // note: can be different from the layer index in the KV cache
+ uint32_t il;
+
+ ggml_tensor * k;
+ ggml_tensor * v;
+ };
+
+ bool do_defrag = false;
+ bool v_trans = true; // the value tensor is transposed
+
+ // the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())
+ // note: this is not part of the KV state and it's only used to speed-up the find_slot() method
+ uint32_t head = 0;
+
+ const uint32_t n_seq_max = 1;
+
+ // required padding
+ const uint32_t n_pad = 1;
+
+ // SWA
+ const uint32_t n_swa = 0;
+
+ const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
+
+ std::vector<ggml_context_ptr> ctxs;
+ std::vector<ggml_backend_buffer_ptr> bufs;
+
+ llama_kv_cells_unified cells;
+
+ std::vector<kv_layer> layers;
+
+ // model layer id -> KV cache layer id
+ std::unordered_map<int32_t, int32_t> map_layer_ids;
+
+ // defrag
+ struct {
+ std::vector<uint32_t> ids;
+ } defrag_info;
+
+ // return true if cells have been moved
+ bool defrag_prepare(int32_t n_max_nodes);
+
+ size_t total_size() const;
+
+ size_t size_k_bytes() const;
+ size_t size_v_bytes() const;
+
+ bool is_masked_swa(llama_pos p0, llama_pos p1) const;
+
+ 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;
+
+ 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_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);
+};
+
+class llama_kv_cache_unified_state : public llama_memory_state_i {
+public:
+ // used for errors
+ llama_kv_cache_unified_state(llama_memory_status status);
+
+ // used to create a full-cache state
+ llama_kv_cache_unified_state(
+ llama_memory_status status,
+ llama_kv_cache_unified * kv);
+
+ // used to create a state from a batch
+ llama_kv_cache_unified_state(
+ llama_memory_status status,
+ llama_kv_cache_unified * kv,
+ llama_sbatch sbatch,
+ std::vector<uint32_t> heads,
+ std::vector<llama_ubatch> ubatches);
+
+ virtual ~llama_kv_cache_unified_state();
+
+ //
+ // llama_memory_state_i
+ //
+
+ bool next() override;
+ bool apply() override;
+
+ std::vector<int64_t> & out_ids() override;
+
+ llama_memory_status get_status() const override;
+ const llama_ubatch & get_ubatch() const override;
+
+ //
+ // llama_kv_cache_unified_state specific API
+ //
+
+ uint32_t get_n_kv() const;
+
+ // get views of the current state of the cache
+ ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
+ ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
+
+ // store k_cur and v_cur in the cache based on the provided head location
+ ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
+ ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
+
+ void set_input_k_shift(ggml_tensor * dst) const;
+
+ void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
+ void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
+
+private:
+ const llama_memory_status status;
+
+ llama_kv_cache_unified * kv;
+
+ llama_sbatch sbatch;
+
+ // the index of the next ubatch to process
+ size_t i_next = 0;
+
+ std::vector<uint32_t> heads;
+ std::vector<llama_ubatch> ubatches;
+
+ //
+ // data needed for building the compute graph for the current ubatch:
+ //
+
+ // a heuristic, to avoid attending the full cache if it is not yet utilized
+ // as the cache gets filled, the benefit from this heuristic disappears
+ int32_t n_kv;
+
+ // the beginning of the current slot in which the ubatch will be inserted
+ int32_t head;
+};
#include "llama-kv-cache.h"
-
-#include "llama-impl.h"
-#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
-//
-
-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;
-}
-
-llama_kv_cache_unified::llama_kv_cache_unified(
- const llama_model & model,
- layer_filter_cb && filter,
- ggml_type type_k,
- ggml_type type_v,
- bool v_trans,
- bool offload,
- uint32_t kv_size,
- uint32_t n_seq_max,
- uint32_t n_pad,
- uint32_t n_swa,
- llama_swa_type swa_type) :
- model(model), hparams(model.hparams), v_trans(v_trans),
- n_seq_max(n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
-
- GGML_ASSERT(kv_size % n_pad == 0);
-
- // create a context for each buffer type
- std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
- 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*hparams.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;
- };
-
- head = 0;
-
- cells.resize(kv_size);
-
- for (uint32_t il = 0; il < hparams.n_layer; il++) {
- if (filter && !filter(il)) {
- LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
- continue;
- }
-
- const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
- const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + 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(il);
- buft = ggml_backend_dev_buffer_type(dev);
-
- dev_name = ggml_backend_dev_name(dev);
- }
-
- LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, il, 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_tensor * v;
-
- k = ggml_new_tensor_2d(ctx, type_k, n_embd_k_gqa, kv_size);
- v = ggml_new_tensor_2d(ctx, type_v, n_embd_v_gqa, kv_size);
-
- ggml_format_name(k, "cache_k_l%d", il);
- ggml_format_name(v, "cache_v_l%d", il);
-
- map_layer_ids[il] = layers.size();
- layers.push_back({ il, k, 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");
- }
-
- 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);
-
- ggml_backend_buffer_clear(buf, 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: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
- (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max,
- ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
- ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
- }
-}
-
-void llama_kv_cache_unified::clear() {
- cells.reset();
-
- head = 0;
-
- for (auto & buf : bufs) {
- ggml_backend_buffer_clear(buf.get(), 0);
- }
-}
-
-bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
- uint32_t new_head = cells.size();
-
- if (p0 < 0) {
- p0 = 0;
- }
-
- if (p1 < 0) {
- p1 = std::numeric_limits<llama_pos>::max();
- }
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- if (!cells.pos_in(i, p0, p1)) {
- continue;
- }
-
- if (cells.seq_has(i, seq_id) && cells.seq_rm(i, seq_id)) {
- if (new_head == cells.size()) {
- new_head = i;
- }
- }
- }
-
- // If we freed up a slot, set head to it so searching can start there.
- if (new_head != cells.size() && new_head < head) {
- head = new_head;
- }
-
- return true;
-}
-
-void llama_kv_cache_unified::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();
- }
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- if (!cells.pos_in(i, p0, p1)) {
- continue;
- }
-
- if (cells.seq_has(i, seq_id_src)) {
- cells.seq_add(i, seq_id_dst);
- }
- }
-}
-
-void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) {
- uint32_t new_head = cells.size();
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- if (cells.seq_keep(i, seq_id)) {
- if (new_head == cells.size()) {
- new_head = i;
- }
- }
- }
-
- // If we freed up a slot, set head to it so searching can start there.
- if (new_head != cells.size() && new_head < head) {
- head = new_head;
- }
-}
-
-void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
- if (shift == 0) {
- return;
- }
-
- uint32_t new_head = cells.size();
-
- 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 all cells.
- if (p0 == p1) {
- return;
- }
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- if (!cells.pos_in(i, p0, p1)) {
- continue;
- }
-
- if (cells.seq_has(i, seq_id)) {
- if (cells.pos_add(i, shift)) {
- if (new_head == cells.size()) {
- new_head = i;
- }
- }
- }
- }
-
- // If we freed up a slot, set head to it so searching can start there.
- // Otherwise we just start the next search from the beginning.
- head = new_head != cells.size() ? new_head : 0;
-}
-
-void llama_kv_cache_unified::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 (uint32_t i = 0; i < cells.size(); ++i) {
- if (!cells.pos_in(i, p0, p1)) {
- continue;
- }
-
- if (cells.seq_has(i, seq_id)) {
- cells.pos_div(i, d);
- }
- }
-}
-
-llama_pos llama_kv_cache_unified::seq_pos_min(llama_seq_id seq_id) const {
- return cells.seq_pos_min(seq_id);
-}
-
-llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
- return cells.seq_pos_max(seq_id);
-}
-
-void llama_kv_cache_unified::restore() {
- for (auto & state : recovery.states) {
- cells.set(state.i, state.cells);
- }
-
- recovery.clear();
-}
-
-void llama_kv_cache_unified::commit() {
- if (recovery.states.empty()) {
- LLAMA_LOG_WARN("%s: the recovery information upon a commit was empty - might indicate a bug (ref: %s)\n",
- __func__, "https://github.com/ggml-org/llama.cpp/pull/13194");
- return;
- }
-
- recovery.clear();
-}
-
-bool llama_kv_cache_unified::update(llama_context & lctx) {
- bool need_reserve = false;
-
- auto * sched = lctx.get_sched();
-
- if (cells.get_has_shift()) {
- if (!get_can_shift()) {
- GGML_ABORT("The current KV cache / model configuration does not support K-shift");
- }
-
- LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__);
-
- // apply K-shift if needed
- if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
- ggml_backend_sched_reset(sched);
-
- auto * gf = lctx.graph_init();
-
- auto res = build_graph_shift(lctx.get_cparams(), lctx.get_ctx_compute(), gf);
-
- ggml_backend_sched_alloc_graph(sched, gf);
-
- res->set_inputs(nullptr);
-
- lctx.graph_compute(gf, false);
-
- need_reserve = true;
- }
-
- cells.reset_shift();
- }
-
- if (do_defrag) {
- LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
-
- if (defrag_prepare(lctx.graph_max_nodes())) {
- ggml_backend_sched_reset(sched);
-
- auto * gf = lctx.graph_init();
-
- auto res = build_graph_defrag(lctx.get_cparams(), lctx.get_ctx_compute(), gf);
-
- ggml_backend_sched_alloc_graph(sched, gf);
-
- res->set_inputs(nullptr);
-
- lctx.graph_compute(gf, false);
-
- need_reserve = true;
- }
-
- 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(cells.get_used() + n_pad)/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 = cells.size();
-
- // when simulating a full KV cache, the specific value of the "head" pointer is not important because it does not
- // affect the shapes of the tensors in the compute graph - it only affects the offsets of the K/V views.
- // we should only guarantee that the head position won't cause out-of-bounds view of the K, V tensors, so
- // setting it to 0 is the simplest way to achieve that
- // ref: https://github.com/ggml-org/llama.cpp/issues/13359
- head = 0;
-}
-
-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;
-
- // 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 > cells.get_used() + 2*ubatch.n_tokens) {
- head = 0;
- }
-
- // otherwise, one cell per token.
-
- if (n_tokens > cells.size()) {
- LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
- return false;
- }
-
-//#define FIND_SLOT_DEBUG 1
-#if FIND_SLOT_DEBUG
- LLAMA_LOG_WARN("begin: n = %5d, used = %5d, head = %5d, n_swa = %5d\n", n, used, head, n_swa);
-
- // for debugging
- {
- std::string ss;
- if (n_swa > 0) {
- for (uint32_t i = 0; i < size; ++i) {
- if (cells.is_empty(i)) {
- ss += '.';
- } else {
- ss += 'x';
- }
- if (i%256 == 255) {
- ss += '\n';
- }
- }
- }
- LLAMA_LOG_WARN("\n%s\n", ss.c_str());
- }
-#endif
-
- uint32_t n_tested = 0;
-
- while (true) {
- if (head + n_tokens > cells.size()) {
- n_tested += cells.size() - head;
- head = 0;
- continue;
- }
-
- bool found = true;
- for (uint32_t i = 0; i < n_tokens; i++) {
- // TODO: improve to accept cells that are masked by the SWA
- if (!cells.is_empty(head + i)) {
- found = false;
- head += i + 1;
- n_tested += i + 1;
- break;
- }
- }
-
- if (found) {
- break;
- }
-
- if (n_tested >= cells.size()) {
- //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
- return false;
- }
- }
-
- // store the old state of the cells in the recovery stack
- recovery.states.push_back({head, cells.cp(head, n_tokens)});
-
- for (uint32_t i = 0; i < n_tokens; ++i) {
- cells.pos_set(head + i, ubatch.pos[i]);
-
- for (int32_t j = 0; j < ubatch.n_seq_id[i]; j++) {
- cells.seq_add(head + i, ubatch.seq_id[i][j]);
- }
- }
-
- // 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(cells.size(), std::max(n_pad, GGML_PAD(cells.used_max_p1(), n_pad)));
-
-#ifdef FIND_SLOT_DEBUG
- LLAMA_LOG_WARN("end: n = %5d, used = %5d, head = %5d, n_swa = %5d\n", n, used, head, n_swa);
-#endif
-
- return true;
-}
-
-bool llama_kv_cache_unified::get_can_shift() const {
- return true;
-}
-
-uint32_t llama_kv_cache_unified::get_n() const {
- return n;
-}
-
-uint32_t llama_kv_cache_unified::get_size() const {
- return cells.size();
-}
-
-ggml_tensor * llama_kv_cache_unified::get_k(ggml_context * ctx, int32_t il) const {
- const int32_t ikv = map_layer_ids.at(il);
-
- auto * k = layers[ikv].k;
-
- return ggml_view_3d(ctx, k,
- hparams.n_embd_head_k, hparams.n_head_kv(il), n,
- ggml_row_size(k->type, hparams.n_embd_head_k),
- ggml_row_size(k->type, hparams.n_embd_k_gqa(il)),
- 0);
-}
-
-ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il) const {
- const int32_t ikv = map_layer_ids.at(il);
-
- auto * v = layers[ikv].v;
-
- if (!v_trans) {
- // note: v->nb[1] <= v->nb[2]
- return ggml_view_3d(ctx, v,
- hparams.n_embd_head_v, hparams.n_head_kv(il), n,
- ggml_row_size(v->type, hparams.n_embd_head_v), // v->nb[1]
- ggml_row_size(v->type, hparams.n_embd_v_gqa(il)), // v->nb[2]
- 0);
- }
-
- // note: v->nb[1] > v->nb[2]
- return ggml_view_3d(ctx, v,
- n, hparams.n_head_kv(il), hparams.n_embd_head_v,
- ggml_row_size(v->type, v->ne[1]*hparams.n_embd_head_v), // v->nb[1]
- ggml_row_size(v->type, v->ne[1]), // v->nb[2]
- 0);
-}
-
-ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
- const int32_t ikv = map_layer_ids.at(il);
-
- auto * k = layers[ikv].k;
-
- const int64_t n_tokens = k_cur->ne[2];
-
- ggml_tensor * k_view = ggml_view_1d(ctx, k,
- n_tokens*hparams.n_embd_k_gqa(il),
- ggml_row_size(k->type, hparams.n_embd_k_gqa(il))*head);
-
- return ggml_cpy(ctx, k_cur, k_view);
-}
-
-ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
- const int32_t ikv = map_layer_ids.at(il);
-
- auto * v = layers[ikv].v;
-
- const int64_t n_tokens = v_cur->ne[2];
-
- v_cur = ggml_reshape_2d(ctx, v_cur, hparams.n_embd_v_gqa(il), n_tokens);
-
- ggml_tensor * v_view = nullptr;
-
- if (!v_trans) {
- v_view = ggml_view_1d(ctx, v,
- n_tokens*hparams.n_embd_v_gqa(il),
- ggml_row_size(v->type, hparams.n_embd_v_gqa(il))*head);
- } else {
- // note: the V cache is transposed when not using flash attention
- v_view = ggml_view_2d(ctx, v, n_tokens, hparams.n_embd_v_gqa(il),
- (v->ne[1])*ggml_element_size(v),
- ( head)*ggml_element_size(v));
-
- v_cur = ggml_transpose(ctx, v_cur);
- }
-
- return ggml_cpy(ctx, v_cur, v_view);
-}
-
-void llama_kv_cache_unified::prune_swa(llama_seq_id seq_id, llama_pos pmin, llama_pos pmax) {
- // no pruning is needed when the cache does not use SWA
- GGML_ASSERT(swa_type != LLAMA_SWA_TYPE_NONE && "do not prune non-SWA cache");
-
- int n_attended = 0;
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- if (!cells.seq_has(i, seq_id)) {
- continue;
- }
-
- const llama_pos p0 = cells.pos_get(i);
-
- if (p0 <= pmin && !is_masked_swa(p0, pmin)) {
- n_attended++;
- }
-
- if (is_masked_swa(p0, pmax)) {
- cells.seq_rm(i, seq_id);
- }
- }
-
- if (n_attended < std::min<int>(n_swa, pmin)) {
- LLAMA_LOG_WARN("%s: partial SWA cache detected - possible loss of information, pmin = %d, n_attended = %d, n_swa = %d\n", __func__, pmin, n_attended, n_swa);
- }
-}
-
-void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
- const int64_t n_tokens = ubatch->n_tokens;
- const int64_t n_seq_tokens = ubatch->n_seq_tokens;
- const int64_t n_seqs = ubatch->n_seqs;
-
- GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
- float * data = (float *) dst->data;
-
- const int64_t n_kv = n;
-
- // Use only the previous KV cells of the correct sequence for each token of the ubatch.
- // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
- // Example with a cache of 10 tokens, 2 tokens populated in cache and 3 tokens in batch:
- // Causal mask:
- // xxx-------
- // xxxx------
- // xxxxx-----
- // Non-causal mask:
- // xxxxx-----
- // xxxxx-----
- // xxxxx-----
- // To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
- for (int h = 0; h < 1; ++h) {
- for (int s = 0; s < n_seqs; ++s) {
- const llama_seq_id seq_id = ubatch->seq_id[s][0];
-
- for (int j = 0; j < n_seq_tokens; ++j) {
- const llama_pos p1 = ubatch->pos[s*n_seq_tokens + j];
-
- for (int i = 0; i < n_kv; ++i) {
- float f = 0.0f;
-
- bool masked = false;
-
- if (cells.is_empty(i)) {
- masked = true;
- } else {
- const llama_pos p0 = cells.pos_get(i);
-
- // mask the token if not the same sequence
- masked = masked || (!cells.seq_has(i, seq_id));
-
- // mask future tokens
- masked = masked || (causal_attn && p0 > p1);
-
- // apply SWA if any
- masked = masked || (is_masked_swa(p0, p1));
-
- if (!masked && hparams.use_alibi) {
- f = -std::abs(p0 - p1);
- }
- }
-
- if (masked) {
- f = -INFINITY;
- }
-
- data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
- }
- }
- }
-
- // mask padded tokens
- if (data) {
- for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
- for (int j = 0; j < n_kv; ++j) {
- data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
- }
- }
- }
- }
-}
-
-void llama_kv_cache_unified::set_input_k_shift(ggml_tensor * dst) const {
- GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
-
- int32_t * data = (int32_t *) dst->data;
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- data[i] = cells.is_empty(i) ? 0 : cells.get_shift(i);
- }
-}
-
-void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
- const int64_t n_tokens = ubatch->n_tokens;
-
- GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
- GGML_ASSERT(!ubatch->equal_seqs); // TODO: use ubatch->n_seqs instead of failing
-
- int32_t * data = (int32_t *) dst->data;
-
- const int64_t n_kv = n;
-
- for (int h = 0; h < 1; ++h) {
- for (int j = 0; j < n_tokens; ++j) {
- for (int i = 0; i < n_kv; ++i) {
- // the position when the cells is empty is irrelevant - it will be masked out later in the attention
- const llama_pos p0 = cells.is_empty(i) ? -1 : cells.pos_get(i);
-
- data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(p0, ubatch->pos[j], hparams.n_rel_attn_bkts, false);
- }
- }
- }
-}
-
-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 {
- size_t size_k_bytes = 0;
-
- for (const auto & layer : layers) {
- size_k_bytes += ggml_nbytes(layer.k);
- }
-
- return size_k_bytes;
-}
-
-size_t llama_kv_cache_unified::size_v_bytes() const {
- size_t size_v_bytes = 0;
-
- for (const auto & layer : layers) {
- size_v_bytes += ggml_nbytes(layer.v);
- }
-
- return size_v_bytes;
-}
-
-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 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 & 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(ctx, cur, GGML_TYPE_F32);
-
- 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);
-
- 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);
- }
-
- 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) {
- kv_self->set_input_k_shift(k_shift);
- }
-}
-
-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>();
-
- 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>(this);
-
- inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, cparams.n_ctx);
- ggml_set_input(inp->k_shift);
-
- for (const auto & layer : layers) {
- const uint32_t il = 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 float freq_base_l = model.get_rope_freq_base (cparams, il);
- const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
-
- ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
- ggml_tensor * k =
- ggml_view_3d(ctx, layer.k,
- n_embd_head_k, n_head_kv, cells.size(),
- ggml_row_size(layer.k->type, n_embd_head_k),
- ggml_row_size(layer.k->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 (const auto & layer : layers) {
- const uint32_t il = layer.il;
-
- 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, layer.k,
- n_embd_k_gqa, nm,
- ggml_row_size(layer.k->type, n_embd_k_gqa),
- ggml_row_size(layer.k->type, n_embd_k_gqa*i));
-
- ggml_tensor * view_k_dst = ggml_view_2d(ctx, layer.k,
- n_embd_k_gqa, nm,
- ggml_row_size(layer.k->type, n_embd_k_gqa),
- ggml_row_size(layer.k->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, layer.v,
- n_embd_v_gqa, nm,
- ggml_row_size(layer.v->type, n_embd_v_gqa),
- ggml_row_size(layer.v->type, n_embd_v_gqa*i));
-
- view_v_dst = ggml_view_2d(ctx, layer.v,
- n_embd_v_gqa, nm,
- ggml_row_size(layer.v->type, n_embd_v_gqa),
- ggml_row_size(layer.v->type, n_embd_v_gqa*id));
- } else {
- view_v_src = ggml_view_2d(ctx, layer.v,
- nm, n_embd_v_gqa,
- ggml_row_size(layer.v->type, cells.size()),
- ggml_row_size(layer.v->type, i));
-
- view_v_dst = ggml_view_2d(ctx, layer.v,
- nm, n_embd_v_gqa,
- ggml_row_size(layer.v->type, cells.size()),
- ggml_row_size(layer.v->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 = layers.size();
-
- const uint32_t n_kv = cells.used_max_p1();
- const uint32_t n_used = cells.get_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) {
- if (!cells.is_empty(i0)) {
- 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.is_empty(i0 + nh)) {
- nh++;
- }
-
- uint32_t nf = 0;
- uint32_t is = n_kv - 1;
-
- // starting from the end, find nh non-empty cells
- for (; is > i0; --is) {
- if (cells.is_empty(is) || ids[is] != n_kv) {
- continue;
- }
-
- // non-empty cell which is not yet moved
- nf++;
-
- if (nf == nh) {
- break;
- }
- }
-
- // this can only happen if `n_used` is not accurate, which would be a bug
- GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
-
- nf = 0;
-
- uint32_t i1 = is;
-
- // are we moving a continuous block of memory?
- bool cont = false;
-
- // should we stop searching for the next move?
- bool stop = false;
-
- // go back and move the nf cells to the hole
- for (; i1 < n_kv; ++i1) {
- if (cells.is_empty(i1) || ids[i1] != n_kv) {
- if (n_moves == max_moves) {
- stop = true;
- break;
- }
-
- cont = false;
- continue;
- }
-
- // this cell goes to (i0 + nf)
- ids[i1] = i0 + nf;
-
- // move the cell meta data
- cells.mv(i1, i0 + nf);
-
- head = n_used;
-
- if (!cont) {
- n_moves++;
- cont = true;
- }
-
- nf++;
-
- if (nf == nh) {
- break;
- }
- }
-
- if (stop || n_moves == max_moves) {
- break;
- }
-
- //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
-
- i0 += nh - 1;
- }
-
- if (n_moves == 0) {
- return false;
- }
-
- LLAMA_LOG_DEBUG("%s: (tmp log) KV defrag cell moves: %u\n", __func__, n_moves);
-
- LLAMA_LOG_DEBUG("%s: expected gf nodes: %u\n", __func__, 6*n_moves*n_layer);
-
- return true;
-}
-
-bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
- assert(p0 >= 0 && p1 >= 0);
-
- switch (swa_type) {
- case LLAMA_SWA_TYPE_NONE:
- {
- } break;
- case LLAMA_SWA_TYPE_STANDARD:
- {
- if (p1 - p0 >= (int32_t) n_swa) {
- return true;
- }
- } break;
- case LLAMA_SWA_TYPE_CHUNKED:
- {
- const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
-
- if (p0 < pos_chunk_start) {
- return true;
- }
- } break;
- }
-
- return false;
-}
-
-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;
-
- // 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 = cells.size();
-
- for (uint32_t i = 0; i < cells.size(); ++i) {
- if (!cells.is_empty(i) && (seq_id == -1 || cells.seq_has(i, seq_id))) {
- ++cell_count;
- if (cell_range_begin == cells.size()) {
- cell_range_begin = i;
- }
- } else {
- if (cell_range_begin != cells.size()) {
- cell_ranges.emplace_back(cell_range_begin, i);
- cell_range_begin = cells.size();
- }
- }
- }
-
- if (cell_range_begin != cells.size()) {
- cell_ranges.emplace_back(cell_range_begin, cells.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_unified::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_unified::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) {
- std::vector<llama_seq_id> seq_ids;
-
- for (llama_seq_id cur = 0; cur < (int) n_seq_max; ++cur) {
- if (cur == seq_id || seq_id == -1) {
- if (cells.seq_has(i, cur)) {
- seq_ids.push_back(cur);
- }
- }
- }
-
- const llama_pos pos = cells.pos_get(i);
- const uint32_t n_seq_id = seq_ids.size();
-
- io.write(&pos, sizeof(pos));
- io.write(&n_seq_id, sizeof(n_seq_id));
-
- for (const auto & seq_id : seq_ids) {
- io.write(&seq_id, sizeof(seq_id));
- }
- }
- }
-}
-
-void llama_kv_cache_unified::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 = this->v_trans ? 1 : 0;
- const uint32_t n_layer = layers.size();
-
- 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 (const auto & layer : layers) {
- const uint32_t il = 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)layer.k->type;
- io.write(&k_type_i, sizeof(k_type_i));
-
- // Write row size of key
- const uint64_t k_size_row = ggml_row_size(layer.k->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(layer.k, range.first * k_size_row, buf_size);
- }
- }
-
- if (!v_trans) {
- for (const auto & layer : layers) {
- const uint32_t il = 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)layer.v->type;
- io.write(&v_type_i, sizeof(v_type_i));
-
- // Write row size of value
- const uint64_t v_size_row = ggml_row_size(layer.v->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(layer.v, 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 = cells.size();
-
- for (const auto & layer : layers) {
- const uint32_t il = 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)layer.v->type;
- io.write(&v_type_i, sizeof(v_type_i));
-
- // Write element size
- const uint32_t v_size_el = ggml_type_size(layer.v->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(layer.v, src_offset, buf_size);
- }
- }
- }
- }
-}
-
-bool llama_kv_cache_unified::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;
-
- 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 != 1) {
- LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
- return false;
- }
-
- // read the sequence id, but directly discard it - we will use dest_seq_id instead
- {
- llama_seq_id seq_id;
- io.read_to(&seq_id, sizeof(seq_id));
- }
-
- batch.pos[i] = pos;
- batch.n_seq_id[i] = n_seq_id;
- batch.seq_id[i] = &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 <= cells.size());
- GGML_ASSERT(cells.pos_get(head) == batch.pos[0]);
- GGML_ASSERT(cells.pos_get(head + cell_count - 1) == batch.pos[cell_count - 1]);
- GGML_ASSERT(cells.seq_has(head, dest_seq_id));
- GGML_ASSERT(cells.seq_has(head + cell_count - 1, dest_seq_id));
- } else {
- // whole KV cache restore
-
- if (cell_count > cells.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) {
- llama_pos pos;
- uint32_t n_seq_id;
-
- io.read_to(&pos, sizeof(pos));
- io.read_to(&n_seq_id, sizeof(n_seq_id));
-
- cells.pos_set(i, pos);
-
- for (uint32_t j = 0; j < n_seq_id; ++j) {
- llama_seq_id seq_id;
- io.read_to(&seq_id, sizeof(seq_id));
-
- if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
- LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, n_seq_max);
- return false;
- }
-
- cells.seq_add(i, seq_id);
- }
- }
-
- head = 0;
- }
-
- return true;
-}
-
-bool llama_kv_cache_unified::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 != layers.size()) {
- LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, (uint32_t) layers.size());
- return false;
- }
- if (cell_count > cells.size()) {
- LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, cells.size());
- return false;
- }
- 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 (const auto & layer : layers) {
- const uint32_t il = 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) layer.k->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(layer.k->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(layer.k, io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
- }
- }
-
- if (!this->v_trans) {
- for (const auto & layer : layers) {
- const uint32_t il = 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)layer.v->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(layer.v->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(layer.v, 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 (const auto & layer : layers) {
- const uint32_t il = 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)layer.v->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(layer.v->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 * cells.size()) * v_size_el;
- ggml_backend_tensor_set(layer.v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
- }
- }
- }
- }
-
- return true;
-}
-
-//
-// llama_kv_cache_unified_iswa
-//
-
-llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
- const llama_model & model,
- ggml_type type_k,
- ggml_type type_v,
- bool v_trans,
- bool offload,
- bool swa_full,
- uint32_t kv_size,
- uint32_t n_seq_max,
- uint32_t n_batch,
- uint32_t n_pad) : hparams(model.hparams) {
- llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
- llama_kv_cache_unified::layer_filter_cb filter_swa = [&](int32_t il) { return model.hparams.is_swa(il); };
-
- const uint32_t size_base = kv_size;
-
- uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, n_pad));
-
- // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size and disable pruning
- if (swa_full) {
- LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n",
- __func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
-
- size_swa = size_base;
- do_prune = false;
- }
-
- LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base);
-
- kv_base = std::make_unique<llama_kv_cache_unified>(
- model, std::move(filter_base), type_k, type_v,
- v_trans, offload, size_base, n_seq_max, n_pad,
- 0, LLAMA_SWA_TYPE_NONE);
-
- LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa);
-
- kv_swa = std::make_unique<llama_kv_cache_unified>(
- model, std::move(filter_swa), type_k, type_v,
- v_trans, offload, size_swa, n_seq_max, n_pad,
- hparams.n_swa, hparams.swa_type);
-}
-
-void llama_kv_cache_unified_iswa::clear() {
- kv_base->clear();
- kv_swa ->clear();
-}
-
-bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
- bool res = true;
-
- res = res & kv_base->seq_rm(seq_id, p0, p1);
- res = res & kv_swa ->seq_rm(seq_id, p0, p1);
-
- return res;
-}
-
-void llama_kv_cache_unified_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
- kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1);
- kv_swa ->seq_cp(seq_id_src, seq_id_dst, p0, p1);
-}
-
-void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) {
- kv_base->seq_keep(seq_id);
- kv_swa ->seq_keep(seq_id);
-}
-
-void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
- kv_base->seq_add(seq_id, p0, p1, shift);
- kv_swa ->seq_add(seq_id, p0, p1, shift);
-}
-
-void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
- kv_base->seq_div(seq_id, p0, p1, d);
- kv_swa ->seq_div(seq_id, p0, p1, d);
-}
-
-llama_pos llama_kv_cache_unified_iswa::seq_pos_min(llama_seq_id seq_id) const {
- // the base cache is a superset of the SWA cache, so we can just check the SWA cache
- return kv_swa->seq_pos_min(seq_id);
-}
-
-llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
- return kv_swa->seq_pos_max(seq_id);
-}
-
-void llama_kv_cache_unified_iswa::restore() {
- kv_base->restore();
- kv_swa ->restore();
-}
-
-void llama_kv_cache_unified_iswa::commit() {
- kv_base->commit();
- kv_swa ->commit();
-
- // slide the attention window, forgetting/pruning old tokens that are outside the window
- if (do_prune) {
- for (const auto & [seq_id, entry] : pending.pos) {
- kv_swa->prune_swa(seq_id, entry.pmin, entry.pmax);
- }
-
- }
-
- pending.clear();
-}
-
-bool llama_kv_cache_unified_iswa::update(llama_context & lctx) {
- bool res = true;
-
- res = res & kv_base->update(lctx);
- res = res & kv_swa ->update(lctx);
-
- return res;
-}
-
-void llama_kv_cache_unified_iswa::defrag_sched(float thold) {
- kv_base->defrag_sched(thold);
- kv_swa ->defrag_sched(thold);
-}
-
-void llama_kv_cache_unified_iswa::set_full() {
- kv_base->set_full();
- kv_swa ->set_full();
-}
-
-llama_sbatch llama_kv_cache_unified_iswa::sbatch_init(const llama_batch & batch, bool logits_all) {
- pending.clear();
-
- if (do_prune) {
- for (int i = 0; i < batch.n_tokens; ++i) {
- for (int s = 0; s < batch.n_seq_id[i]; ++s) {
- const llama_seq_id seq_id = batch.seq_id[i][s];
- const llama_pos pos = batch.pos[i];
-
- if (pending.pos.find(seq_id) == pending.pos.end()) {
- pending.pos[seq_id].pmin = pos;
- pending.pos[seq_id].pmax = pos;
- } else {
- pending.pos[seq_id].pmin = std::min(pending.pos[seq_id].pmin, pos);
- pending.pos[seq_id].pmax = std::max(pending.pos[seq_id].pmax, pos);
- }
- }
- }
- }
-
- return llama_sbatch(batch, hparams.n_embd, true, logits_all);
-}
-
-llama_ubatch llama_kv_cache_unified_iswa::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_iswa::find_slot(const llama_ubatch & batch) {
- bool res = true;
-
- res = res & kv_base->find_slot(batch);
- res = res & kv_swa ->find_slot(batch);
-
- return res;
-}
-
-bool llama_kv_cache_unified_iswa::get_can_shift() const {
- return kv_base->get_size() == kv_swa->get_size();
-}
-
-void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
- kv_base->state_write(io, seq_id);
- kv_swa ->state_write(io, seq_id);
-}
-
-void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
- kv_base->state_read(io, seq_id);
- kv_swa ->state_read(io, seq_id);
-}
-
-llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_kv_base() const {
- return kv_base.get();
-}
-
-llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_kv_swa() const {
- return kv_swa.get();
-}
-
-//
-// 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,
- uint32_t n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
- const int32_t n_layer = hparams.n_layer;
-
- LLAMA_LOG_INFO("%s: kv_size = %u, n_seq_max = %u, type_k = '%s', type_v = '%s', n_layer = %d\n",
- __func__, kv_size, n_seq_max, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
-
- head = 0;
- size = kv_size;
- used = 0;
-
- 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 shift) {
- if (shift == 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 += shift;
- }
- }
- }
-}
-
-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_min(llama_seq_id seq_id) const {
- llama_pos result = std::numeric_limits<llama_pos>::max();
-
- for (uint32_t i = 0; i < size; ++i) {
- if (cells[i].has_seq_id(seq_id)) {
- result = std::min(result, cells[i].pos);
- }
- }
-
- if (result == std::numeric_limits<llama_pos>::max()) {
- result = -1;
- }
-
- return result;
-}
-
-llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const {
- llama_pos result = -1;
-
- 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 & ctx) {
- GGML_UNUSED(ctx);
- return false;
-}
-
-void llama_kv_cache_recurrent::defrag_sched(float thold) {
- GGML_UNUSED(thold);
- // noop
-}
-
-void llama_kv_cache_recurrent::set_full() {
- n = size;
- head = 0;
-}
-
-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=%u Try using a bigger --parallel value\n", __func__, seq_id, n_seq_max);
- 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;
-}
-
-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;
- }
-
- // 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 (!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;
-}
#include "llama.h"
#include "llama-io.h"
-#include "llama-graph.h"
#include "llama-memory.h"
-#include "llama-kv-cells.h"
-
-#include "ggml-cpp.h"
-
-#include <set>
-#include <unordered_map>
-#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 {
virtual ~llama_kv_cache() = default;
- // call if batch processing fails - restores the cache state
- virtual void restore() = 0;
+ // split the input batch into a set of ubatches and verify that they can fit into the cache
+ // return a state object containing the ubatches and KV cache state required to process them
+ // check the llama_memory_state_i::get_status() for the result
+ virtual llama_memory_state_ptr init_batch(
+ const llama_batch & batch,
+ uint32_t n_ubatch,
+ bool embd_pooled,
+ bool logits_all) = 0;
- // call after successful batch processing - clears any pending state
- virtual void commit() = 0;
+ // simulate full cache, used for allocating worst-case compute buffers
+ virtual llama_memory_state_ptr init_full() = 0;
// process any pending defrag/shift/etc. operations
// optionally call once before processing a new batch
+ // return true if any operations were performed
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
- // TODO: remove
- virtual void set_full() = 0;
-
+ // TODO: change to
+ // llama_memory_state_ptr init_defrag(float thold) = 0;
//
- // batch processing
- //
-
- // =============================================================================================================
- // TODO: refactor and simplify this [TAG: KV_API]
-
- 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;
-
- // =============================================================================================================
+ virtual void defrag_sched(float thold) = 0;
// getters
virtual bool get_can_shift() const = 0;
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_guard() {
- kv->restore();
- }
-
- void commit() {
- kv->commit();
- }
-
-private:
- llama_kv_cache * kv;
-};
-
-//
-// llama_kv_cache_unified
-//
-
-class llama_kv_cache_unified : public llama_kv_cache {
-public:
- static uint32_t get_padding(const llama_cparams & cparams);
-
- // this callback is used to filter out layers that should not be included in the cache
- using layer_filter_cb = std::function<bool(int32_t il)>;
-
- llama_kv_cache_unified(
- const llama_model & model,
- layer_filter_cb && filter,
- ggml_type type_k,
- ggml_type type_v,
- bool v_trans,
- bool offload,
- uint32_t kv_size,
- uint32_t n_seq_max,
- uint32_t n_pad,
- uint32_t n_swa,
- llama_swa_type swa_type);
-
- ~llama_kv_cache_unified() = 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 shift) override;
- void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
-
- llama_pos seq_pos_min(llama_seq_id seq_id) const 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 & 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;
-
- // 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) override;
-
- bool get_can_shift() const override;
-
- // 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;
-
- //
- // llama_kv_cache_unified specific API
- //
-
- uint32_t get_n() const;
- uint32_t get_size() const;
-
- // get views of the current state of the cache
- ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
- ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
-
- // store k_cur and v_cur in the cache based on the current head location
- ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
- ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
-
- void prune_swa(llama_seq_id seq_id, llama_pos pmin, llama_pos pmax);
-
- void set_input_kq_mask (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
- void set_input_k_shift (ggml_tensor * dst) const;
- void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
-
-private:
- const llama_model & model;
- const llama_hparams & hparams;
-
- struct kv_layer {
- // layer index in the model
- // note: can be different from the layer index in the KV cache
- uint32_t il;
-
- ggml_tensor * k;
- ggml_tensor * v;
- };
-
- bool do_defrag = false;
- bool v_trans = true; // the value tensor is transposed
-
- uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
-
- // computed before each graph build
- // TODO: cells should start to maintain this value dynamically based on the edits
- uint32_t n = 0;
-
- const uint32_t n_seq_max = 1;
-
- // required padding
- const uint32_t n_pad = 1;
-
- // SWA
- const uint32_t n_swa = 0;
-
- const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
-
- std::vector<ggml_context_ptr> ctxs;
- std::vector<ggml_backend_buffer_ptr> bufs;
-
- llama_kv_cells_unified cells;
-
- std::vector<kv_layer> layers;
-
- // model layer id -> KV cache layer id
- std::unordered_map<int32_t, int32_t> map_layer_ids;
-
- // recovery information used to restore the KV cells to their original state in case of a failure
- // TODO: do not store as a state in the llama_kv_cache object, instead return upon batch preparation
- // to achieve that, first need to refactor the llama_kv_cache interface [TAG: KV_API]
- struct {
- void clear() {
- states.clear();
- }
-
- struct state {
- uint32_t i;
-
- llama_kv_cells_unified cells;
- };
-
- // stack with the partial states before each ubatch
- std::vector<state> states;
- } recovery;
-
- // defrag
- struct {
- std::vector<uint32_t> ids;
- } defrag_info;
-
- // return true if cells have been moved
- bool defrag_prepare(int32_t n_max_nodes);
-
- size_t total_size() const;
-
- size_t size_k_bytes() const;
- size_t size_v_bytes() const;
-
- bool is_masked_swa(llama_pos p0, llama_pos p1) const;
-
- 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;
-
- 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_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);
-};
-
-//
-// llama_kv_cache_unified_iswa
-//
-
-// utilizes two instances of llama_kv_cache_unified
-// the first instance is for the non-SWA layers of the model and the second instance is for the SWA layers
-// upon successful commit, the SWA cache removes old tokens outside the n_swa window
-
-class llama_kv_cache_unified_iswa : public llama_kv_cache {
-public:
- llama_kv_cache_unified_iswa(
- const llama_model & model,
- ggml_type type_k,
- ggml_type type_v,
- bool v_trans,
- bool offload,
- bool swa_full,
- uint32_t kv_size,
- uint32_t n_seq_max,
- uint32_t n_batch,
- uint32_t n_pad);
-
- ~llama_kv_cache_unified_iswa() = 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 shift) override;
- void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
-
- llama_pos seq_pos_min(llama_seq_id seq_id) const 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 & 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;
-
- bool find_slot(const llama_ubatch & batch) override;
-
- bool get_can_shift() const override;
-
- // 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;
-
- //
- // llama_kv_cache_unified_iswa specific API
- //
-
- llama_kv_cache_unified * get_kv_base() const;
- llama_kv_cache_unified * get_kv_swa () const;
-
-private:
- const llama_hparams & hparams;
-
- bool do_prune = true;
-
- struct {
- struct entry {
- llama_pos pmin;
- llama_pos pmax;
- };
-
- void clear() {
- pos.clear();
- }
-
- // used to perform SWA pruning of old tokens
- std::unordered_map<llama_seq_id, entry> pos;
- } pending;
-
- std::unique_ptr<llama_kv_cache_unified> kv_base;
- std::unique_ptr<llama_kv_cache_unified> kv_swa;
-};
-
-//
-// llama_kv_cache_recurrent
-//
-
-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,
- uint32_t n_seq_max);
-
- ~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 shift) override;
- void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override;
-
- llama_pos seq_pos_min(llama_seq_id seq_id) const 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 & 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;
-
- bool find_slot(const llama_ubatch & batch) 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;
-
- uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
- uint32_t size = 0; // total number of cells, shared across all sequences
- 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;
-
- // 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;
-
- const uint32_t n_seq_max = 1;
-
- 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_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);
-};
// the index of the last cell that is used + 1
// return 0 if no cells are used
uint32_t used_max_p1() const {
-#if 0
- if (!seq_pos[0].empty()) printf("kv_cells: min[0] = %5d, max[0] = %5d\n", *seq_pos[0].begin(), *seq_pos[0].rbegin());
- if (!seq_pos[1].empty()) printf("kv_cells: min[1] = %5d, max[1] = %5d\n", *seq_pos[1].begin(), *seq_pos[1].rbegin());
- if (!seq_pos[2].empty()) printf("kv_cells: min[2] = %5d, max[2] = %5d\n", *seq_pos[2].begin(), *seq_pos[2].rbegin());
-#endif
-
return used.empty() ? 0 : *used.rbegin() + 1;
}
}
}
+ // clear a non-empty cell
+ void rm(uint32_t i) {
+ assert(i < pos.size());
+ assert(pos[i] != -1);
+
+ seq_pos_rm(i);
+
+ pos[i] = -1;
+ seq[i].reset();
+
+ used.erase(i);
+ }
+
// note: call only if the cell has seq_id
// return true if the cell becomes empty
bool seq_rm(uint32_t i, llama_seq_id seq_id) {
return false;
}
+ // number of different sequences in the cell
+ int seq_count(uint32_t i) const {
+ assert(i < pos.size());
+ assert(pos[i] != -1);
+
+ return seq[i].count();
+ }
+
+ // check if the cell contains seq_id
bool seq_has(uint32_t i, llama_seq_id seq_id) const {
assert(i < pos.size());
assert(seq_id >= 0);
seq_pos[seq_id].insert(pos[i]);
}
+ // return the sequence id of this cell
+ // note: call only for cells with exactly one sequence
+ llama_seq_id seq_get(uint32_t i) const {
+ assert(seq[i].count() == 1);
+
+ for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+ if (seq[i].test(s)) {
+ return s;
+ }
+ }
+
+ return -1;
+ }
+
// the minimum position of sequence seq_id currently present in any of the cells
// return -1 if the sequence is not present
llama_pos seq_pos_min(llama_seq_id seq_id) const {
void pos_set(uint32_t i, llama_pos p) {
assert(i < pos.size());
assert(pos[i] == -1);
+ assert(seq[i].none());
pos[i] = p;
#include "llama.h"
+#include <memory>
+#include <vector>
+
+struct llama_ubatch;
+
struct llama_memory_params {
// kv cache
ggml_type type_k;
virtual bool get_can_edit() const = 0;
};
+
+enum llama_memory_status {
+ LLAMA_MEMORY_STATUS_SUCCESS = 0,
+ LLAMA_MEMORY_STATUS_FAILED_PREPARE,
+ LLAMA_MEMORY_STATUS_FAILED_COMPUTE,
+};
+
+// the interface for managing the memory state during batch processing
+// this interface is implemented per memory type. see:
+// - llama_kv_cache_unified_state
+// - llama_kv_cache_unified_iswa_state
+// ...
+//
+// the only method that can mutate the memory and the memory state is llama_memory_i::apply()
+//
+// TODO: rename to llama_memory_context_i ?
+class llama_memory_state_i {
+public:
+ virtual ~llama_memory_state_i() = default;
+
+ // consume the current ubatch from the state and proceed to the next one
+ // return false if we are done
+ virtual bool next() = 0;
+
+ // apply the memory state for the current ubatch to the memory object
+ // return false on failure
+ virtual bool apply() = 0;
+
+ // TODO: this might get reworked in the future when refactoring llama_batch
+ virtual std::vector<int64_t> & out_ids() = 0;
+
+ // get the current ubatch
+ virtual const llama_ubatch & get_ubatch() const = 0;
+
+ // get the status of the memory state
+ virtual llama_memory_status get_status() const = 0;
+};
+
+using llama_memory_state_ptr = std::unique_ptr<llama_memory_state_i>;
#include "llama-batch.h"
#include "llama-cparams.h"
#include "llama-model-loader.h"
-#include "llama-kv-cache.h"
+
+#include "llama-kv-cache-unified.h"
+#include "llama-kv-cache-unified-iswa.h"
+#include "llama-kv-cache-recurrent.h"
#include "ggml-cpp.h"
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
+ ml.get_arr_n(LLM_KV_CLASSIFIER_OUTPUT_LABELS, hparams.n_cls_out, false);
switch (hparams.n_layer) {
case 3:
case LLM_ARCH_NOMIC_BERT_MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
- type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0);
+ type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, TENSOR_NOT_REQUIRED);
if (arch == LLM_ARCH_BERT) {
pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0);
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
- cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
- cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, TENSOR_NOT_REQUIRED);
+ cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+ cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
}
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
- if (arch == LLM_ARCH_BERT) {
+ layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
+ layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
+
+ if (!layer.wqkv) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
- } else {
- layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
- }
-
- if (arch == LLM_ARCH_NOMIC_BERT_MOE) {
- layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
inpL = build_inp_embd(model.tok_embd);
// token types are hardcoded to zero ("Sentence A")
- ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
- inpL = ggml_add(ctx0, inpL, type_row0);
+ if (model.type_embd) {
+ ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
+ inpL = ggml_add(ctx0, inpL, type_row0);
+ }
if (model.arch == LLM_ARCH_BERT) {
inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
}
ggml_tensor * Vcur;
// self-attention
- if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
- Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq);
-
- if (model.layers[il].attn_q_norm) {
- Qcur = build_norm(Qcur,
- model.layers[il].attn_q_norm,
- model.layers[il].attn_q_norm_b,
- LLM_NORM, il);
- }
-
- Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk);
-
- if (model.layers[il].attn_k_norm) {
- Kcur = build_norm(Kcur,
- model.layers[il].attn_k_norm,
- model.layers[il].attn_k_norm_b,
- LLM_NORM, il);
- }
-
- Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv);
-
- Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
- Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
- Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
- } else {
- // compute Q and K and RoPE them
+ if (model.layers[il].wqkv) {
cur = build_lora_mm(model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
- if (model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
+ if (model.layers[il].bqkv) {
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
}
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+ } else {
+ Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq);
+ Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk);
+ Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv);
+ }
- Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
- Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
- Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+ if (model.layers[il].attn_q_norm) {
+ Qcur = build_norm(Qcur,
+ model.layers[il].attn_q_norm,
+ model.layers[il].attn_q_norm_b,
+ LLM_NORM, il);
+ }
+ if (model.layers[il].attn_k_norm) {
+ Kcur = build_norm(Kcur,
+ model.layers[il].attn_k_norm,
+ model.layers[il].attn_k_norm_b,
+ LLM_NORM, il);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ // RoPE
+ if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
- const auto kv_head = kv_self->head;
+ const auto kv_head = kv_state->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
- ggml_tensor * conv_states_all = kv_self->k_l[il];
- ggml_tensor * ssm_states_all = kv_self->v_l[il];
+ ggml_tensor * conv_states_all = kv_state->get_k_l(il);
+ ggml_tensor * ssm_states_all = kv_state->get_v_l(il);
// (ab)using the KV cache to store the states
ggml_tensor * conv = build_copy_mask_state(
ggml_tensor * state_mask,
const llama_ubatch & ubatch,
int il) const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
const auto n_head = n_embd / head_size;
const auto n_head_kv = hparams.n_head_kv(il);
- const auto kv_head = kv_self->head;
+ const auto kv_head = kv_state->get_head();
const auto & layer = model.layers[il];
}
ggml_tensor * wkv_state = build_copy_mask_state(
- gf, kv_self->v_l[il], state_copy, state_mask,
+ gf, kv_state->get_v_l(il), state_copy, state_mask,
hparams.n_embd_v_s(), n_seqs);
ggml_tensor * wkv_output;
wkv_state,
ggml_view_1d(
ctx0,
- kv_self->v_l[il],
+ kv_state->get_v_l(il),
hparams.n_embd_v_s() * n_seqs,
- hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self->v_l[il])
+ hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_state->get_v_l(il))
)
)
);
ggml_tensor *& first_layer_value,
const llama_ubatch & ubatch,
int il) const {
- const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+ const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
const auto head_count = n_embd / head_size;
const auto n_seq_tokens = ubatch.n_seq_tokens;
- const auto kv_head = kv_self->head;
+ const auto kv_head = kv_state->get_head();
const auto & layer = model.layers[il];
a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
ggml_tensor * wkv_state = build_copy_mask_state(
- gf, kv_self->v_l[il], state_copy, state_mask,
+ gf, kv_state->get_v_l(il), state_copy, state_mask,
hparams.n_embd_v_s(), n_seqs);
ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
wkv_state,
ggml_view_1d(
ctx0,
- kv_self->v_l[il],
+ kv_state->get_v_l(il),
hparams.n_embd_v_s() * n_seqs,
- hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self->v_l[il])
+ hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_state->get_v_l(il))
)
)
);
params.swa_full,
cparams.n_ctx,
cparams.n_seq_max,
- cparams.n_batch,
+ cparams.n_ubatch,
padding);
} else {
GGML_ASSERT(!hparams.is_swa_any());
switch (arch) {
case LLM_ARCH_LLAMA:
- case LLM_ARCH_MINICPM:
{
llm = std::make_unique<llm_build_llama>(*this, params, gf);
} break;
} break;
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
+ case LLM_ARCH_MINICPM:
{
llm = std::make_unique<llm_build_granite>(*this, params, gf);
} break;
return model->hparams.n_head_kv();
}
+int32_t llama_model_n_swa(const llama_model * model) {
+ return model->hparams.n_swa;
+}
+
// deprecated
int32_t llama_n_ctx_train(const llama_model * model) {
return llama_model_n_ctx_train(model);
llama_token * token;
float * embd;
llama_pos * pos;
- int32_t * n_seq_id;
- llama_seq_id ** seq_id;
- int8_t * logits; // TODO: rename this to "output"
+ int32_t * n_seq_id; // TODO: remove, should belong to only 1 sequence
+ llama_seq_id ** seq_id; // TODO: become llama_seq_id * seq_id;
+ int8_t * logits; // TODO: rename this to "output"
} llama_batch;
enum llama_model_kv_override_type {
bool no_perf; // measure performance timings
bool op_offload; // offload host tensor operations to device
bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
+ // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases
+ // ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573
};
// model quantization parameters
LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head (const struct llama_model * model);
LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model);
+ LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model);
// Get the model's RoPE frequency scaling factor
LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
// If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
- // - explicitly with llama_kv_self_update()
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_self_seq_add(
// Integer division of the positions by factor of `d > 1`
// If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
- // - explicitly with llama_kv_self_update()
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_self_seq_div(
// Returns the smallest position present in the KV cache for the specified sequence
// This is typically non-zero only for SWA caches
+ // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache
// Return -1 if the sequence is empty
LLAMA_API llama_pos llama_kv_self_seq_pos_min(
struct llama_context * ctx,
llama_seq_id seq_id);
// Returns the largest position present in the KV cache for the specified sequence
+ // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache
// Return -1 if the sequence is empty
LLAMA_API llama_pos llama_kv_self_seq_pos_max(
struct llama_context * ctx,
// Defragment the KV cache
// This will be applied:
// - lazily on next llama_decode()
- // - explicitly with llama_kv_self_update()
- LLAMA_API void llama_kv_self_defrag(struct llama_context * ctx);
+ LLAMA_API DEPRECATED(void llama_kv_self_defrag(struct llama_context * ctx),
+ "simply remove this call, the context will automatically decide when to do a defragmentation based on 'defrag_thold'");
// Check if the context supports KV cache shifting
LLAMA_API bool llama_kv_self_can_shift(const struct llama_context * ctx);
// Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
- LLAMA_API void llama_kv_self_update(struct llama_context * ctx);
+ LLAMA_API DEPRECATED(void llama_kv_self_update(struct llama_context * ctx),
+ "simply remove this call, updates are applied lazily on the next llama_decode()");
//
// State / sessions
overview / pkg / ggml / sources / whisper.cpp / commitdiff