mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-31 14:13:09 +01:00
6369f867a4
This commit renames the `batch` parameter to `ubatch` in the
`llama_kv_cache_find_slot`, `llm_build_inp_embd`, and
`llm_build_mamba` functions.
The motivation for this is that this should have been done as part of
Commit 19d900a756 ("llama : rename batch
to ubatch (#9950)") but for some reason I missed these functions in
that commit and only noticed them now (sorry).
719 lines
25 KiB
C++
719 lines
25 KiB
C++
#include "llama-kv-cache.h"
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#include "llama-impl.h"
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#include "llama-batch.h"
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#include "llama-cparams.h"
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#include "llama-model.h"
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#include <algorithm>
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#include <limits>
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#include <map>
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static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false};
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uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) {
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// the FA kernels require padding to avoid extra runtime boundary checks
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return cparams.flash_attn ? 256u : 32u;
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}
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bool llama_kv_cache_init(
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struct llama_kv_cache & cache,
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const llama_model & model,
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const llama_cparams & cparams,
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ggml_type type_k,
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ggml_type type_v,
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uint32_t kv_size,
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bool offload) {
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const struct llama_hparams & hparams = model.hparams;
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const int32_t n_layer = hparams.n_layer;
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cache.has_shift = false;
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cache.recurrent = llama_model_is_recurrent(&model);
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cache.v_trans = !cache.recurrent && !cparams.flash_attn;
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cache.can_shift = !cache.recurrent && model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA
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LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d\n",
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__func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, cache.can_shift);
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cache.head = 0;
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cache.size = kv_size;
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cache.used = 0;
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cache.type_k = type_k;
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cache.type_v = type_v;
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cache.cells.clear();
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cache.cells.resize(kv_size);
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// create a context for each buffer type
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
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auto it = ctx_map.find(buft);
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if (it == ctx_map.end()) {
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struct ggml_init_params params = {
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/*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()),
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context * ctx = ggml_init(params);
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if (!ctx) {
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return nullptr;
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}
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ctx_map[buft] = ctx;
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cache.ctxs.emplace_back(ctx);
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return ctx;
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}
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return it->second;
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};
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cache.k_l.reserve(n_layer);
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cache.v_l.reserve(n_layer);
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for (int i = 0; i < n_layer; i++) {
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const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
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LLAMA_LOG_DEBUG("%s: layer %d: n_embd_k_gqa = %d, n_embd_v_gqa = %d\n", __func__, i, n_embd_k_gqa, n_embd_v_gqa);
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ggml_backend_buffer_type_t buft;
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if (offload) {
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auto * dev = model.dev_layer.at(i).dev;
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buft = ggml_backend_dev_buffer_type(dev);
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} else {
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buft = ggml_backend_cpu_buffer_type();
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}
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ggml_context * ctx = ctx_for_buft(buft);
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if (!ctx) {
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LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__);
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return false;
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}
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ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
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ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
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ggml_format_name(k, "cache_k_l%d", i);
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ggml_format_name(v, "cache_v_l%d", i);
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cache.k_l.push_back(k);
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cache.v_l.push_back(v);
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}
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// allocate tensors and initialize the buffers to avoid NaNs in the padding
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for (auto it : ctx_map) {
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auto * buft = it.first;
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auto * ctx = it.second;
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
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if (!buf) {
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LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
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return false;
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}
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ggml_backend_buffer_clear(buf, 0);
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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);
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cache.bufs.emplace_back(buf);
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}
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return true;
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}
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struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
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struct llama_kv_cache & cache,
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const struct llama_ubatch & ubatch) {
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const uint32_t n_tokens = ubatch.n_tokens;
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const uint32_t n_seqs = ubatch.n_seqs;
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const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
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if (cache.recurrent) {
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// For recurrent state architectures (like Mamba or RWKV),
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// each cache cell can store the state for a whole sequence.
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// A slot should be always be contiguous.
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// can only process batches with an equal number of new tokens in each sequence
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GGML_ASSERT(ubatch.equal_seqs);
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int32_t min = cache.size - 1;
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int32_t max = 0;
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// everything should fit if all seq_ids are smaller than the max
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for (uint32_t s = 0; s < n_seqs; ++s) {
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const uint32_t n_seq_id = ubatch.n_seq_id[s];
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for (uint32_t j = 0; j < n_seq_id; ++j) {
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const llama_seq_id seq_id = ubatch.seq_id[s][j];
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if (seq_id < 0 || (uint32_t) seq_id >= cache.size) {
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// too big seq_id
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// TODO: would it be possible to resize the cache instead?
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LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
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return llama_kv_cache_slot_info_failed;
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}
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if (j > 0) {
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llama_kv_cell & seq = cache.cells[seq_id];
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if (seq.tail >= 0) {
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llama_kv_cell & cell = cache.cells[seq.tail];
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// clear cells from seq_ids that become shared
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// (should not normally happen, but let's handle it anyway)
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cell.seq_id.erase(seq_id);
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seq.tail = -1;
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if (cell.seq_id.empty()) {
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cell.pos = -1;
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cell.src = -1;
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cache.used -= 1;
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}
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}
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}
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}
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}
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#ifndef NDEBUG
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{
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std::vector<int32_t> tails_verif;
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tails_verif.assign(cache.size, -1);
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for (uint32_t i = 0; i < cache.size; ++i) {
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llama_kv_cell & cell = cache.cells[i];
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for (llama_seq_id seq_id : cell.seq_id) {
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if (tails_verif[seq_id] != -1) {
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LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]);
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}
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tails_verif[seq_id] = i;
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}
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}
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for (uint32_t i = 0; i < cache.size; ++i) {
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if (tails_verif[i] != cache.cells[i].tail) {
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LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cache.cells[i].tail, tails_verif[i]);
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}
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}
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}
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#endif
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// find next empty cell
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uint32_t next_empty_cell = cache.head;
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for (uint32_t i = 0; i < cache.size; ++i) {
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if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; }
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llama_kv_cell & cell = cache.cells[next_empty_cell];
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if (cell.is_empty()) { break; }
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next_empty_cell += 1;
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}
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// find usable cell range
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for (uint32_t s = 0; s < n_seqs; ++s) {
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const llama_seq_id seq_id = ubatch.seq_id[s][0];
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llama_kv_cell & seq_meta = cache.cells[seq_id];
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bool has_cell = false;
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if (seq_meta.tail >= 0) {
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llama_kv_cell & cell = cache.cells[seq_meta.tail];
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GGML_ASSERT(cell.has_seq_id(seq_id));
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// does this seq_id "own" the cell?
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if (cell.seq_id.size() == 1) { has_cell = true; }
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}
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if (!has_cell) {
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llama_kv_cell & empty_cell = cache.cells[next_empty_cell];
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GGML_ASSERT(empty_cell.is_empty());
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// copy old tail into the empty cell
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if (seq_meta.tail >= 0) {
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llama_kv_cell & orig_cell = cache.cells[seq_meta.tail];
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empty_cell.pos = orig_cell.pos;
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empty_cell.src = orig_cell.src;
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orig_cell.seq_id.erase(seq_id);
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empty_cell.seq_id.insert(seq_id); // will be overwritten
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}
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seq_meta.tail = next_empty_cell;
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// find next empty cell
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if (s + 1 < n_seqs) {
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next_empty_cell += 1;
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for (uint32_t i = 0; i < cache.size; ++i) {
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if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; }
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llama_kv_cell & cell = cache.cells[next_empty_cell];
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if (cell.is_empty()) { break; }
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next_empty_cell += 1;
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}
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}
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}
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if (min > seq_meta.tail) { min = seq_meta.tail; }
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if (max < seq_meta.tail) { max = seq_meta.tail; }
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}
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// gather and re-order
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for (uint32_t s = 0; s < n_seqs; ++s) {
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int32_t dst_id = s + min;
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int32_t src_id = cache.cells[ubatch.seq_id[s][0]].tail;
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if (dst_id != src_id) {
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llama_kv_cell & dst_cell = cache.cells[dst_id];
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llama_kv_cell & src_cell = cache.cells[src_id];
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std::swap(dst_cell.pos, src_cell.pos);
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std::swap(dst_cell.src, src_cell.src);
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std::swap(dst_cell.seq_id, src_cell.seq_id);
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// swap tails (assuming they NEVER overlap)
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for (const llama_seq_id seq_id : src_cell.seq_id) {
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cache.cells[seq_id].tail = src_id;
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}
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for (const llama_seq_id seq_id : dst_cell.seq_id) {
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cache.cells[seq_id].tail = dst_id;
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}
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}
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}
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// update the pos of the used seqs
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for (uint32_t s = 0; s < n_seqs; ++s) {
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const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
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int32_t cell_id = s + min;
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llama_kv_cell & cell = cache.cells[cell_id];
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if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) {
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// What should happen when the pos backtracks or skips a value?
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// Clearing the state mid-batch would require special-casing which isn't done.
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LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
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__func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
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}
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cell.pos = last_pos;
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cell.seq_id.clear();
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for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
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const llama_seq_id seq_id = ubatch.seq_id[s][j];
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cell.seq_id.insert(seq_id);
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cache.cells[seq_id].tail = cell_id;
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}
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}
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// allow getting the range of used cells, from head to head + n
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cache.head = min;
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cache.n = max - min + 1;
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cache.used = std::count_if(cache.cells.begin(), cache.cells.end(),
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[](const llama_kv_cell& cell){ return !cell.is_empty(); });
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// sanity check
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return llama_kv_cache_slot_info(cache.n >= n_seqs);
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}
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// otherwise, one cell per token.
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if (n_tokens > cache.size) {
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LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
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return llama_kv_cache_slot_info_failed;
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}
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uint32_t n_tested = 0;
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while (true) {
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if (cache.head + n_tokens > cache.size) {
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n_tested += cache.size - cache.head;
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cache.head = 0;
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continue;
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}
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bool found = true;
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for (uint32_t i = 0; i < n_tokens; i++) {
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if (cache.cells[cache.head + i].pos >= 0) {
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found = false;
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cache.head += i + 1;
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n_tested += i + 1;
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break;
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}
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}
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if (found) {
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break;
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}
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if (n_tested >= cache.size) {
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//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
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return llama_kv_cache_slot_info_failed;
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}
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}
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for (uint32_t s = 0; s < n_seqs; s++) {
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for (uint32_t i = 0; i < n_seq_tokens; ++i) {
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uint32_t k = s*n_seq_tokens + i;
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cache.cells[cache.head + k].pos = ubatch.pos[k];
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for (int32_t j = 0; j < ubatch.n_seq_id[s]; j++) {
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cache.cells[cache.head + k].seq_id.insert(ubatch.seq_id[s][j]);
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}
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}
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}
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cache.used += n_tokens;
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return llama_kv_cache_slot_info(cache.head, cache.head + n_tokens);
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}
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uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
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for (uint32_t i = cache.size; i > 0; --i) {
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const llama_kv_cell & cell = cache.cells[i - 1];
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if (cell.pos >= 0 && !cell.is_empty()) {
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return i;
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}
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}
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return 0;
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}
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void llama_kv_cache_clear(struct llama_kv_cache & cache) {
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for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
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cache.cells[i].pos = -1;
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cache.cells[i].seq_id.clear();
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cache.cells[i].src = -1;
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cache.cells[i].tail = -1;
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}
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cache.head = 0;
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cache.used = 0;
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for (auto & buf : cache.bufs) {
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ggml_backend_buffer_clear(buf.get(), 0);
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}
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}
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bool llama_kv_cache_seq_rm(
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struct llama_kv_cache & cache,
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llama_seq_id seq_id,
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llama_pos p0,
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llama_pos p1) {
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uint32_t new_head = cache.size;
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if (p0 < 0) p0 = 0;
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if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
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// models like Mamba or RWKV can't have a state partially erased
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if (cache.recurrent) {
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if (seq_id >= (int64_t) cache.size) {
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// could be fatal
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return false;
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}
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if (0 <= seq_id) {
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int32_t & tail_id = cache.cells[seq_id].tail;
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if (tail_id >= 0) {
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const llama_kv_cell & cell = cache.cells[tail_id];
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// partial intersection is invalid
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if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
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return false;
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}
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// invalidate tails which will be cleared
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if (p0 <= cell.pos && cell.pos < p1) {
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tail_id = -1;
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}
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}
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} else {
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// seq_id is negative, then the range should include everything or nothing
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if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
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return false;
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}
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}
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}
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for (uint32_t i = 0; i < cache.size; ++i) {
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if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
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if (seq_id < 0) {
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cache.cells[i].seq_id.clear();
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} else if (cache.cells[i].has_seq_id(seq_id)) {
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cache.cells[i].seq_id.erase(seq_id);
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} else {
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continue;
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}
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if (cache.cells[i].is_empty()) {
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// keep count of the number of used cells
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if (cache.cells[i].pos >= 0) cache.used--;
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cache.cells[i].pos = -1;
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cache.cells[i].src = -1;
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if (new_head == cache.size) new_head = i;
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}
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}
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}
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// If we freed up a slot, set head to it so searching can start there.
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if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
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return true;
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}
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void llama_kv_cache_seq_cp(
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struct llama_kv_cache & cache,
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llama_seq_id seq_id_src,
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llama_seq_id seq_id_dst,
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llama_pos p0,
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llama_pos p1) {
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if (p0 < 0) p0 = 0;
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if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
|
|
|
|
if (cache.recurrent) {
|
|
if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
|
|
llama_kv_cell & tail_src = cache.cells[seq_id_src];
|
|
llama_kv_cell & tail_dst = cache.cells[seq_id_dst];
|
|
if (tail_dst.tail >= 0) {
|
|
// clear destination seq_id if it wasn't empty
|
|
llama_kv_cell & cell_dst = cache.cells[tail_dst.tail];
|
|
|
|
cell_dst.seq_id.erase(seq_id_dst);
|
|
tail_dst.tail = -1;
|
|
if (cell_dst.seq_id.empty()) {
|
|
cell_dst.pos = -1;
|
|
cell_dst.delta = -1;
|
|
cell_dst.src = -1;
|
|
cache.used -= 1;
|
|
}
|
|
}
|
|
if (tail_src.tail >= 0) {
|
|
llama_kv_cell & cell_src = cache.cells[tail_src.tail];
|
|
|
|
cell_src.seq_id.insert(seq_id_dst);
|
|
tail_dst.tail = tail_src.tail;
|
|
}
|
|
}
|
|
|
|
return;
|
|
}
|
|
// otherwise, this is the KV cache of a Transformer-like model
|
|
|
|
cache.head = 0;
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
cache.cells[i].seq_id.insert(seq_id_dst);
|
|
}
|
|
}
|
|
}
|
|
|
|
void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
|
|
uint32_t new_head = cache.size;
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.recurrent && (llama_seq_id) i != seq_id) {
|
|
cache.cells[i].tail = -1;
|
|
}
|
|
if (!cache.cells[i].has_seq_id(seq_id)) {
|
|
if (cache.cells[i].pos >= 0) cache.used--;
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].src = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
if (new_head == cache.size) new_head = i;
|
|
} else {
|
|
cache.cells[i].seq_id.clear();
|
|
cache.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 != cache.size && new_head < cache.head) cache.head = new_head;
|
|
}
|
|
|
|
void llama_kv_cache_seq_add(
|
|
struct llama_kv_cache & cache,
|
|
llama_seq_id seq_id,
|
|
llama_pos p0,
|
|
llama_pos p1,
|
|
llama_pos delta) {
|
|
uint32_t new_head = cache.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 the cache.
|
|
if (p0 == p1) return;
|
|
|
|
if (cache.recurrent) {
|
|
// for Mamba-like or RWKV models, only the pos needs to be shifted
|
|
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
|
|
const int32_t tail_id = cache.cells[seq_id].tail;
|
|
if (tail_id >= 0) {
|
|
llama_kv_cell & cell = cache.cells[tail_id];
|
|
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
|
|
cell.pos += delta;
|
|
}
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
cache.has_shift = true;
|
|
cache.cells[i].pos += delta;
|
|
cache.cells[i].delta += delta;
|
|
|
|
if (cache.cells[i].pos < 0) {
|
|
if (!cache.cells[i].is_empty()) {
|
|
cache.used--;
|
|
}
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
if (new_head == cache.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.
|
|
cache.head = new_head != cache.size ? new_head : 0;
|
|
}
|
|
|
|
void llama_kv_cache_seq_div(
|
|
struct llama_kv_cache & cache,
|
|
llama_seq_id seq_id,
|
|
llama_pos p0,
|
|
llama_pos p1,
|
|
int d) {
|
|
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;
|
|
|
|
if (cache.recurrent) {
|
|
// for Mamba-like or RWKV models, only the pos needs to be changed
|
|
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
|
|
const int32_t tail_id = cache.cells[seq_id].tail;
|
|
if (tail_id >= 0) {
|
|
llama_kv_cell & cell = cache.cells[tail_id];
|
|
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
|
|
cell.pos /= d;
|
|
}
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
cache.has_shift = true;
|
|
|
|
{
|
|
llama_pos p_old = cache.cells[i].pos;
|
|
cache.cells[i].pos /= d;
|
|
cache.cells[i].delta += cache.cells[i].pos - p_old;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
|
|
llama_pos result = 0;
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id)) {
|
|
result = std::max(result, cache.cells[i].pos);
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
|
|
if (!cache.recurrent) {
|
|
cache.do_defrag = true;
|
|
}
|
|
}
|
|
|
|
int32_t llama_get_kv_cache_token_count(const struct llama_kv_cache & kv) {
|
|
int result = 0;
|
|
|
|
for (uint32_t i = 0; i < kv.size; i++) {
|
|
result += kv.cells[i].seq_id.size();
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
int32_t llama_get_kv_cache_used_cells(const struct llama_kv_cache & kv) {
|
|
return kv.used;
|
|
}
|
|
|
|
bool llama_kv_cache_can_shift(const struct llama_kv_cache & kv) {
|
|
return kv.can_shift;
|
|
}
|
|
|
|
//
|
|
// kv cache view
|
|
//
|
|
|
|
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_kv_cache & kv, int32_t n_seq_max) {
|
|
struct llama_kv_cache_view result = {
|
|
/*.n_cells = */ 0,
|
|
/*.n_seq_max = */ n_seq_max,
|
|
/*.token_count = */ 0,
|
|
/*.used_cells = */ llama_get_kv_cache_used_cells(kv),
|
|
/*.max_contiguous = */ 0,
|
|
/*.max_contiguous_idx = */ -1,
|
|
/*.cells = */ nullptr,
|
|
/*.cells_sequences = */ nullptr,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
|
|
if (view->cells != nullptr) {
|
|
free(view->cells);
|
|
view->cells = nullptr;
|
|
}
|
|
if (view->cells_sequences != nullptr) {
|
|
free(view->cells_sequences);
|
|
view->cells_sequences = nullptr;
|
|
}
|
|
}
|
|
|
|
void llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_kv_cache & kv) {
|
|
if (uint32_t(view->n_cells) < kv.size || view->cells == nullptr) {
|
|
view->n_cells = int32_t(kv.size);
|
|
void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
|
|
GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
|
|
view->cells = (struct llama_kv_cache_view_cell *)p;
|
|
p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
|
|
GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
|
|
view->cells_sequences = (llama_seq_id *)p;
|
|
}
|
|
|
|
const std::vector<llama_kv_cell> & kv_cells = kv.cells;
|
|
llama_kv_cache_view_cell * c_curr = view->cells;
|
|
llama_seq_id * cs_curr = view->cells_sequences;
|
|
int32_t used_cells = 0;
|
|
int32_t token_count = 0;
|
|
int32_t curr_contig_idx = -1;
|
|
uint32_t max_contig = 0;
|
|
int32_t max_contig_idx = -1;
|
|
|
|
for (int32_t i = 0; i < int32_t(kv.size); i++, c_curr++, cs_curr += view->n_seq_max) {
|
|
const size_t curr_size = kv_cells[i].seq_id.size();
|
|
token_count += curr_size;
|
|
c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
|
|
|
|
if (curr_size > 0) {
|
|
if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
|
|
max_contig = i - curr_contig_idx;
|
|
max_contig_idx = curr_contig_idx;
|
|
}
|
|
curr_contig_idx = -1;
|
|
} else if (curr_contig_idx < 0) {
|
|
curr_contig_idx = i;
|
|
}
|
|
|
|
int seq_idx = 0;
|
|
for (const llama_seq_id it : kv_cells[i].seq_id) {
|
|
if (seq_idx >= view->n_seq_max) {
|
|
break;
|
|
}
|
|
cs_curr[seq_idx] = it;
|
|
seq_idx++;
|
|
}
|
|
if (seq_idx != 0) {
|
|
used_cells++;
|
|
}
|
|
for (; seq_idx < view->n_seq_max; seq_idx++) {
|
|
cs_curr[seq_idx] = -1;
|
|
}
|
|
}
|
|
if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
|
|
max_contig_idx = curr_contig_idx;
|
|
max_contig = kv_cells.size() - curr_contig_idx;
|
|
}
|
|
view->max_contiguous = max_contig;
|
|
view->max_contiguous_idx = max_contig_idx;
|
|
view->token_count = token_count;
|
|
view->used_cells = used_cells;
|
|
if (uint32_t(used_cells) != kv.used) {
|
|
LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
|
|
__func__, kv.used, used_cells);
|
|
}
|
|
}
|