mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
1991 lines
63 KiB
C++
1991 lines
63 KiB
C++
#include "llama-kv-cache-unified.h"
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#include "llama-impl.h"
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#include "llama-io.h"
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#include "llama-model.h"
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#include "llama-context.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <limits>
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#include <map>
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#include <stdexcept>
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//
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// llama_kv_cache_unified
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//
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llama_kv_cache_unified::llama_kv_cache_unified(
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const llama_model & model,
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layer_filter_cb && filter,
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ggml_type type_k,
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ggml_type type_v,
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bool v_trans,
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bool offload,
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uint32_t kv_size,
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uint32_t n_seq_max,
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uint32_t n_pad,
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uint32_t n_swa,
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llama_swa_type swa_type) :
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model(model), hparams(model.hparams), v_trans(v_trans),
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n_seq_max(n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
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GGML_ASSERT(kv_size % n_pad == 0);
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// TODO: this is temporary until we support passing reuse layer filters [KV_REUSE]
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auto n_layer_cache = hparams.n_layer;
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if (model.arch == LLM_ARCH_GEMMA3N) {
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n_layer_cache = 20;
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}
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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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ggml_init_params params = {
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/*.mem_size =*/ size_t(2u*n_layer_cache*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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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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head = 0;
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cells.resize(kv_size);
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for (uint32_t il = 0; il < n_layer_cache; il++) {
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if (filter && !filter(il)) {
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LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
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continue;
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}
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const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
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const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
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const char * dev_name = "CPU";
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ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type();
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if (offload) {
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auto * dev = model.dev_layer(il);
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buft = ggml_backend_dev_buffer_type(dev);
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dev_name = ggml_backend_dev_name(dev);
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}
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LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, il, dev_name);
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ggml_context * ctx = ctx_for_buft(buft);
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if (!ctx) {
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throw std::runtime_error("failed to create ggml context for kv cache");
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}
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ggml_tensor * k;
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ggml_tensor * v;
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k = ggml_new_tensor_2d(ctx, type_k, n_embd_k_gqa, kv_size);
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v = ggml_new_tensor_2d(ctx, type_v, n_embd_v_gqa, kv_size);
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ggml_format_name(k, "cache_k_l%d", il);
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ggml_format_name(v, "cache_v_l%d", il);
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map_layer_ids[il] = layers.size();
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layers.push_back({ il, k, v });
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}
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// TODO: this is temporary until we support passing reuse layer filters [KV_REUSE]
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if (model.arch == LLM_ARCH_GEMMA3N) {
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LLAMA_LOG_DEBUG("%s: GEMMA3N: reuse layers [%d, %d]\n", __func__, n_layer_cache, hparams.n_layer - 1);
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for (uint32_t il = n_layer_cache; il < hparams.n_layer; il++) {
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if (filter && !filter(il)) {
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LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, il);
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continue;
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}
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const bool is_swa = hparams.is_swa(il);
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const uint32_t il_reuse = n_layer_cache - (is_swa ? 2 : 1);
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GGML_ASSERT(map_layer_ids.find(il_reuse) != map_layer_ids.end());
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map_layer_ids[il] = map_layer_ids[il_reuse];
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LLAMA_LOG_DEBUG("%s: layer %3d: reuse layer %d, isw = %d\n", __func__, il, il_reuse, is_swa);
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}
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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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throw std::runtime_error("failed to allocate buffer for kv cache");
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}
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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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ggml_backend_buffer_clear(buf, 0);
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bufs.emplace_back(buf);
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}
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{
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const size_t memory_size_k = size_k_bytes();
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const size_t memory_size_v = size_v_bytes();
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LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
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(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max,
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ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
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ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
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}
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const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
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debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;
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const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
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supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) : 0;
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if (!supports_set_rows) {
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LLAMA_LOG_WARN("%s: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility\n", __func__);
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}
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}
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void llama_kv_cache_unified::clear(bool data) {
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cells.reset();
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head = 0;
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if (data) {
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for (auto & buf : bufs) {
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ggml_backend_buffer_clear(buf.get(), 0);
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}
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}
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}
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bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
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uint32_t new_head = cells.size();
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if (p0 < 0) {
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p0 = 0;
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}
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if (p1 < 0) {
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p1 = std::numeric_limits<llama_pos>::max();
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}
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if (seq_id >= 0) {
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for (uint32_t i = 0; i < cells.size(); ++i) {
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if (!cells.pos_in(i, p0, p1)) {
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continue;
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}
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if (cells.seq_has(i, seq_id) && cells.seq_rm(i, seq_id)) {
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if (new_head == cells.size()) {
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new_head = i;
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}
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}
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}
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} else {
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// match any sequence
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for (uint32_t i = 0; i < cells.size(); ++i) {
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if (!cells.pos_in(i, p0, p1)) {
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continue;
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}
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cells.rm(i);
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if (new_head == cells.size()) {
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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 != cells.size() && new_head < head) {
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head = new_head;
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}
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return true;
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}
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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) {
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if (seq_id_src == seq_id_dst) {
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return;
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}
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if (p0 < 0) {
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p0 = 0;
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}
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if (p1 < 0) {
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p1 = std::numeric_limits<llama_pos>::max();
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}
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for (uint32_t i = 0; i < cells.size(); ++i) {
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if (!cells.pos_in(i, p0, p1)) {
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continue;
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}
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if (cells.seq_has(i, seq_id_src)) {
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cells.seq_add(i, seq_id_dst);
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}
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}
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}
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void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) {
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uint32_t new_head = cells.size();
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for (uint32_t i = 0; i < cells.size(); ++i) {
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if (cells.seq_keep(i, seq_id)) {
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if (new_head == cells.size()) {
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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 != cells.size() && new_head < head) {
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head = new_head;
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}
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}
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void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
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if (shift == 0) {
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return;
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}
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uint32_t new_head = cells.size();
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if (p0 < 0) {
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p0 = 0;
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}
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if (p1 < 0) {
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p1 = std::numeric_limits<llama_pos>::max();
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}
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// If there is no range then return early to avoid looping over all cells.
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if (p0 == p1) {
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return;
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}
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for (uint32_t i = 0; i < cells.size(); ++i) {
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if (!cells.pos_in(i, p0, p1)) {
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continue;
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}
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if (cells.seq_has(i, seq_id)) {
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if (cells.pos_add(i, shift)) {
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if (new_head == cells.size()) {
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new_head = i;
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}
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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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// Otherwise we just start the next search from the beginning.
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head = new_head != cells.size() ? new_head : 0;
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}
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void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
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if (d == 1) {
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return;
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}
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if (p0 < 0) {
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p0 = 0;
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}
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if (p1 < 0) {
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p1 = std::numeric_limits<llama_pos>::max();
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}
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// If there is no range then return early to avoid looping over the cache.
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if (p0 == p1) {
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return;
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}
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for (uint32_t i = 0; i < cells.size(); ++i) {
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if (!cells.pos_in(i, p0, p1)) {
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continue;
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}
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if (cells.seq_has(i, seq_id)) {
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cells.pos_div(i, d);
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}
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}
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}
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llama_pos llama_kv_cache_unified::seq_pos_min(llama_seq_id seq_id) const {
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return cells.seq_pos_min(seq_id);
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}
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llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
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return cells.seq_pos_max(seq_id);
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}
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llama_memory_context_ptr llama_kv_cache_unified::init_batch(
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llama_batch_allocr & balloc,
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uint32_t n_ubatch,
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bool embd_all) {
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GGML_UNUSED(embd_all);
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do {
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balloc.split_reset();
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std::vector<llama_ubatch> ubatches;
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while (true) {
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auto ubatch = balloc.split_simple(n_ubatch);
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if (ubatch.n_tokens == 0) {
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break;
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}
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ubatches.push_back(std::move(ubatch)); // NOLINT
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}
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if (balloc.get_n_used() < balloc.get_n_tokens()) {
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// failed to find a suitable split
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break;
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}
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auto sinfos = prepare(ubatches);
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if (sinfos.empty()) {
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break;
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}
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return std::make_unique<llama_kv_cache_unified_context>(
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this, std::move(sinfos), std::move(ubatches));
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} while (false);
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return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
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}
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llama_memory_context_ptr llama_kv_cache_unified::init_full() {
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return std::make_unique<llama_kv_cache_unified_context>(this);
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}
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llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
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bool do_shift = get_has_shift();
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defrag_info dinfo;
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// see if we need to defrag
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{
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bool do_defrag = optimize;
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const auto thold = lctx->get_cparams().defrag_thold;
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if (!do_defrag && thold > 0.0f) {
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const auto n_kv = cells.used_max_p1();
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// - do not defrag small contexts (i.e. < 2048 tokens)
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// - count the padding towards the number of used tokens
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const float fragmentation = n_kv >= 2048 ? std::max(0.0f, 1.0f - (float(cells.get_used() + n_pad)/n_kv)) : 0.0f;
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if (fragmentation > thold) {
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LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation);
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do_defrag = true;
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}
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}
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if (do_defrag) {
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dinfo = defrag_prepare(lctx->graph_max_nodes());
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}
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}
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return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo));
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}
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llama_kv_cache_unified::slot_info_vec_t llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
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llama_kv_cache_unified::slot_info_vec_t res;
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struct state {
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uint32_t head_old; // old position of the head, before placing the ubatch
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slot_info sinfo; // slot info for the ubatch
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llama_kv_cells_unified cells; // copy of the old cells, before placing the ubatch
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};
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// remember the old state of the cells so we can restore it in the end
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std::vector<state> states;
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bool success = true;
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for (const auto & ubatch : ubatches) {
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// non-continuous slots require support for ggml_set_rows()
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const bool cont = supports_set_rows ? false : true;
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// only find a suitable slot for the ubatch. don't modify the cells yet
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const auto sinfo_new = find_slot(ubatch, cont);
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if (sinfo_new.empty()) {
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success = false;
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break;
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}
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// remeber the position that we found
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res.push_back(sinfo_new);
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// store the old state of the cells in the recovery stack
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states.push_back({head, sinfo_new, cells.cp(sinfo_new.idxs)});
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// now emplace the ubatch
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apply_ubatch(sinfo_new, ubatch);
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}
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// iterate backwards and restore the cells to their original state
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for (auto it = states.rbegin(); it != states.rend(); ++it) {
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cells.set(it->sinfo.idxs, it->cells);
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head = it->head_old;
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}
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if (!success) {
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return {};
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}
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return res;
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}
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bool llama_kv_cache_unified::update(llama_context * lctx, bool do_shift, const defrag_info & dinfo) {
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bool updated = false;
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auto * sched = lctx->get_sched();
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if (do_shift) {
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if (!get_can_shift()) {
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GGML_ABORT("The current KV cache / model configuration does not support K-shift");
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}
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LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__);
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// apply K-shift if needed
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if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
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ggml_backend_sched_reset(sched);
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auto * gf = lctx->graph_init();
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auto res = build_graph_shift(lctx->get_cparams(), lctx->get_ctx_compute(), gf);
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if (!res) {
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LLAMA_LOG_ERROR("%s: failed to build graph for K-shift\n", __func__);
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return updated;
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}
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if (!ggml_backend_sched_alloc_graph(sched, gf)) {
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LLAMA_LOG_ERROR("%s: failed to allocate compute graph for K-shift\n", __func__);
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return updated;
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}
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res->set_inputs(nullptr);
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if (lctx->graph_compute(gf, false) != GGML_STATUS_SUCCESS) {
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LLAMA_LOG_ERROR("%s: failed to compute K-shift\n", __func__);
|
|
return updated;
|
|
}
|
|
|
|
updated = true;
|
|
}
|
|
|
|
cells.reset_shift();
|
|
}
|
|
|
|
if (!dinfo.empty()) {
|
|
LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
|
|
|
|
// apply moves:
|
|
{
|
|
const auto n_kv = dinfo.ids.size();
|
|
|
|
for (uint32_t i = 0; i < n_kv; ++i) {
|
|
assert(dinfo.ids[i] <= n_kv);
|
|
|
|
if (dinfo.ids[i] == n_kv || dinfo.ids[i] == i) {
|
|
continue;
|
|
}
|
|
|
|
cells.mv(i, dinfo.ids[i]);
|
|
}
|
|
|
|
// reset the head so we can find the first free slot during the next ubatch
|
|
head = 0;
|
|
}
|
|
|
|
ggml_backend_sched_reset(sched);
|
|
|
|
auto * gf = lctx->graph_init();
|
|
|
|
auto res = build_graph_defrag(lctx->get_cparams(), lctx->get_ctx_compute(), gf, dinfo);
|
|
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;
|
|
}
|
|
|
|
return updated;
|
|
}
|
|
|
|
llama_kv_cache_unified::slot_info llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch, bool cont) const {
|
|
const uint32_t n_tokens = ubatch.n_tokens;
|
|
|
|
uint32_t head_cur = this->head;
|
|
|
|
// 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;
|
|
}
|
|
|
|
if (n_tokens > cells.size()) {
|
|
LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
|
|
return { };
|
|
}
|
|
|
|
if (debug > 0) {
|
|
LLAMA_LOG_DEBUG("%s: n = %5d, used = %5d, head = %5d, size = %5d, n_swa = %5d\n", __func__, cells.used_max_p1(), cells.get_used(), head, get_size(), n_swa);
|
|
|
|
if ((debug == 2 && n_swa > 0) || debug > 2) {
|
|
std::string ss;
|
|
for (uint32_t i = 0; i < cells.size(); ++i) {
|
|
if (cells.is_empty(i)) {
|
|
ss += '.';
|
|
} else {
|
|
assert(cells.seq_count(i) >= 1);
|
|
|
|
if (cells.seq_count(i) == 1) {
|
|
ss += std::to_string(cells.seq_get(i));
|
|
} else {
|
|
ss += 'M';
|
|
}
|
|
}
|
|
if (i%256 == 255) {
|
|
ss += " *";
|
|
ss += '\n';
|
|
}
|
|
}
|
|
LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
|
|
}
|
|
|
|
if ((debug == 2 && n_swa > 0) || debug > 2) {
|
|
std::string ss;
|
|
for (uint32_t i = 0; i < cells.size(); ++i) {
|
|
std::string cur;
|
|
if (cells.is_empty(i)) {
|
|
cur = '.';
|
|
} else {
|
|
cur = std::to_string(cells.pos_get(i));
|
|
}
|
|
const int n = cur.size();
|
|
for (int j = 0; j < 5 - n; ++j) {
|
|
cur += ' ';
|
|
}
|
|
ss += cur;
|
|
if (i%256 == 255) {
|
|
ss += " *";
|
|
}
|
|
if (i%64 == 63) {
|
|
ss += '\n';
|
|
}
|
|
}
|
|
LLAMA_LOG_DEBUG("\n%s\n", ss.c_str());
|
|
}
|
|
|
|
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
|
|
if (cells.seq_pos_min(s) < 0) {
|
|
continue;
|
|
}
|
|
|
|
LLAMA_LOG_DEBUG("%s: min[%d] = %5d, max[%d] = %5d\n", __func__, s, cells.seq_pos_min(s), s, cells.seq_pos_max(s));
|
|
}
|
|
}
|
|
|
|
uint32_t n_tested = 0;
|
|
|
|
// for continuous slots, we test that all tokens in the ubatch fit, starting from the current head
|
|
// for non-continuous slots, we test the tokens one by one
|
|
const uint32_t n_test = cont ? n_tokens : 1;
|
|
|
|
slot_info res;
|
|
|
|
auto & idxs = res.idxs;
|
|
|
|
idxs.reserve(n_tokens);
|
|
|
|
while (true) {
|
|
if (head_cur + n_test > cells.size()) {
|
|
n_tested += cells.size() - head_cur;
|
|
head_cur = 0;
|
|
continue;
|
|
}
|
|
|
|
for (uint32_t i = 0; i < n_test; i++) {
|
|
const auto idx = head_cur;
|
|
|
|
//const llama_pos pos = ubatch.pos[i];
|
|
//const llama_seq_id seq_id = ubatch.seq_id[i][0];
|
|
|
|
// can we use this cell? either:
|
|
// - the cell is empty
|
|
// - the cell is occupied only by one sequence:
|
|
// - (disabled) mask causally, if the sequence is the same as the one we are inserting
|
|
// - mask SWA, using current max pos for that sequence in the cache
|
|
// always insert in the cell with minimum pos
|
|
bool can_use = cells.is_empty(idx);
|
|
|
|
if (!can_use && cells.seq_count(idx) == 1) {
|
|
const llama_pos pos_cell = cells.pos_get(idx);
|
|
|
|
// (disabled) causal mask
|
|
// note: it's better to purge any "future" tokens beforehand
|
|
//if (cells.seq_has(idx, seq_id)) {
|
|
// can_use = pos_cell >= pos;
|
|
//}
|
|
|
|
if (!can_use) {
|
|
const llama_seq_id seq_id_cell = cells.seq_get(idx);
|
|
|
|
// SWA mask
|
|
if (is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
|
|
can_use = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
head_cur++;
|
|
n_tested++;
|
|
|
|
if (can_use) {
|
|
idxs.push_back(idx);
|
|
} else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (idxs.size() == n_tokens) {
|
|
break;
|
|
}
|
|
|
|
if (cont) {
|
|
idxs.clear();
|
|
}
|
|
|
|
if (n_tested >= cells.size()) {
|
|
//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
|
|
return { };
|
|
}
|
|
}
|
|
|
|
// we didn't find a suitable slot - return empty result
|
|
if (idxs.size() < n_tokens) {
|
|
res.clear();
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
void llama_kv_cache_unified::apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch) {
|
|
// keep track of the max sequence position that we would overwrite with this ubatch
|
|
// for non-SWA cache, this would be always empty
|
|
llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
|
|
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
|
|
seq_pos_max_rm[s] = -1;
|
|
}
|
|
|
|
assert(ubatch.n_tokens == sinfo.idxs.size());
|
|
|
|
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
|
|
const auto idx = sinfo.idxs.at(i);
|
|
|
|
if (!cells.is_empty(idx)) {
|
|
assert(cells.seq_count(idx) == 1);
|
|
|
|
const llama_seq_id seq_id = cells.seq_get(idx);
|
|
const llama_pos pos = cells.pos_get(idx);
|
|
|
|
seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
|
|
|
|
cells.rm(idx);
|
|
}
|
|
|
|
cells.pos_set(idx, ubatch.pos[i]);
|
|
|
|
for (int32_t s = 0; s < ubatch.n_seq_id[i]; s++) {
|
|
cells.seq_add(idx, ubatch.seq_id[i][s]);
|
|
}
|
|
}
|
|
|
|
// note: we want to preserve the invariant that all positions between [pos_min, pos_max] for each sequence
|
|
// will be present in the cache. so we have to purge any position which is less than those we would overwrite
|
|
// ref: https://github.com/ggml-org/llama.cpp/pull/13746#issuecomment-2916057092
|
|
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
|
|
if (seq_pos_max_rm[s] == -1) {
|
|
continue;
|
|
}
|
|
|
|
if (cells.seq_pos_min(s) <= seq_pos_max_rm[s]) {
|
|
LLAMA_LOG_DEBUG("%s: purging positions [%d, %d] of sequence %d from KV cache\n",
|
|
__func__, cells.seq_pos_min(s), seq_pos_max_rm[s], s);
|
|
|
|
seq_rm(s, cells.seq_pos_min(s), seq_pos_max_rm[s] + 1);
|
|
}
|
|
}
|
|
|
|
// move the head at the end of the slot
|
|
head = sinfo.idxs.back() + 1;
|
|
}
|
|
|
|
bool llama_kv_cache_unified::get_can_shift() const {
|
|
return true;
|
|
}
|
|
|
|
uint32_t llama_kv_cache_unified::get_size() const {
|
|
return cells.size();
|
|
}
|
|
|
|
bool llama_kv_cache_unified::get_has_shift() const {
|
|
return cells.get_has_shift();
|
|
}
|
|
|
|
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, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
|
|
const int32_t ikv = map_layer_ids.at(il);
|
|
|
|
auto * k = layers[ikv].k;
|
|
|
|
const int64_t n_embd_k_gqa = k->ne[0];
|
|
const int64_t n_tokens = k_cur->ne[2];
|
|
|
|
k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
|
|
|
|
if (k_idxs && supports_set_rows) {
|
|
return ggml_set_rows(ctx, k, k_cur, k_idxs);
|
|
}
|
|
|
|
// TODO: fallback to old ggml_cpy() method for backwards compatibility
|
|
// will be removed when ggml_set_rows() is adopted by all backends
|
|
|
|
ggml_tensor * k_view = ggml_view_1d(ctx, k,
|
|
n_tokens*n_embd_k_gqa,
|
|
ggml_row_size(k->type, n_embd_k_gqa)*sinfo.head());
|
|
|
|
return ggml_cpy(ctx, k_cur, k_view);
|
|
}
|
|
|
|
ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const {
|
|
const int32_t ikv = map_layer_ids.at(il);
|
|
|
|
auto * v = layers[ikv].v;
|
|
|
|
const int64_t n_embd_v_gqa = v->ne[0];
|
|
const int64_t n_tokens = v_cur->ne[2];
|
|
|
|
v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
|
|
|
|
if (v_idxs && supports_set_rows) {
|
|
if (!v_trans) {
|
|
return ggml_set_rows(ctx, v, v_cur, v_idxs);
|
|
}
|
|
|
|
// the row becomes a single element
|
|
ggml_tensor * v_view = ggml_reshape_3d(ctx, v, 1, v->ne[1], v->ne[0]);
|
|
|
|
// note: the V cache is transposed when not using flash attention
|
|
v_cur = ggml_permute(ctx, ggml_reshape_3d(ctx, v_cur, v_cur->ne[0], 1, v_cur->ne[1]), 2, 0, 1, 3);
|
|
|
|
// note: we can be more explicit here at the cost of extra cont
|
|
// however, above we take advantage that a row of single element is always continuous regardless of the row stride
|
|
//v_cur = ggml_transpose(ctx, v_cur);
|
|
//v_cur = ggml_cont_3d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1]);
|
|
|
|
// we broadcast the KV indices n_embd_v_gqa times
|
|
// v [1, n_kv, n_embd_v_gqa]
|
|
// v_cur [1, n_tokens, n_embd_v_gqa]
|
|
// v_idxs [n_tokens, 1, 1]
|
|
return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
|
|
}
|
|
|
|
// TODO: fallback to old ggml_cpy() method for backwards compatibility
|
|
// will be removed when ggml_set_rows() is adopted by all backends
|
|
|
|
ggml_tensor * v_view = nullptr;
|
|
|
|
if (!v_trans) {
|
|
v_view = ggml_view_1d(ctx, v,
|
|
n_tokens*n_embd_v_gqa,
|
|
ggml_row_size(v->type, n_embd_v_gqa)*sinfo.head());
|
|
} else {
|
|
v_cur = ggml_transpose(ctx, v_cur);
|
|
|
|
v_view = ggml_view_2d(ctx, v, n_tokens, n_embd_v_gqa,
|
|
(v->ne[1] )*ggml_element_size(v),
|
|
(sinfo.head())*ggml_element_size(v));
|
|
}
|
|
|
|
return ggml_cpy(ctx, v_cur, v_view);
|
|
}
|
|
|
|
ggml_tensor * llama_kv_cache_unified::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
|
|
const uint32_t n_tokens = ubatch.n_tokens;
|
|
|
|
ggml_tensor * k_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, n_tokens);
|
|
|
|
ggml_set_input(k_idxs);
|
|
|
|
return k_idxs;
|
|
}
|
|
|
|
ggml_tensor * llama_kv_cache_unified::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
|
|
const uint32_t n_tokens = ubatch.n_tokens;
|
|
|
|
ggml_tensor * v_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, n_tokens);
|
|
|
|
ggml_set_input(v_idxs);
|
|
|
|
return v_idxs;
|
|
}
|
|
|
|
void llama_kv_cache_unified::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
|
|
if (!supports_set_rows) {
|
|
return;
|
|
}
|
|
|
|
const uint32_t n_tokens = ubatch->n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
|
|
int64_t * data = (int64_t *) dst->data;
|
|
|
|
for (int64_t i = 0; i < n_tokens; ++i) {
|
|
data[i] = sinfo.idxs.at(i);
|
|
}
|
|
}
|
|
|
|
void llama_kv_cache_unified::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
|
|
if (!supports_set_rows) {
|
|
return;
|
|
}
|
|
|
|
const uint32_t n_tokens = ubatch->n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
|
|
int64_t * data = (int64_t *) dst->data;
|
|
|
|
for (int64_t i = 0; i < n_tokens; ++i) {
|
|
data[i] = sinfo.idxs.at(i);
|
|
}
|
|
}
|
|
|
|
void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
|
|
const uint32_t n_tokens = ubatch->n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
|
|
float * data = (float *) dst->data;
|
|
|
|
const int64_t 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 (uint32_t h = 0; h < 1; ++h) {
|
|
for (uint32_t i = 0; i < n_tokens; ++i) {
|
|
const llama_seq_id seq_id = ubatch->seq_id[i][0];
|
|
|
|
const llama_pos p1 = ubatch->pos[i];
|
|
|
|
for (uint32_t j = 0; j < n_kv; ++j) {
|
|
float f = 0.0f;
|
|
|
|
bool masked = false;
|
|
|
|
if (cells.is_empty(j)) {
|
|
masked = true;
|
|
} else {
|
|
const llama_pos p0 = cells.pos_get(j);
|
|
|
|
// mask the token if not the same sequence
|
|
masked = masked || (!cells.seq_has(j, 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) + i*n_kv + j] = f;
|
|
}
|
|
}
|
|
|
|
// mask padded tokens
|
|
if (data) {
|
|
for (uint32_t 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 i = 0; i < n_tokens; ++i) {
|
|
for (int j = 0; j < n_kv; ++j) {
|
|
// 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(j) ? -1 : cells.pos_get(j);
|
|
|
|
data[h*(n_kv*n_tokens) + i*n_kv + j] = llama_relative_position_bucket(p0, ubatch->pos[i], 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;
|
|
|
|
auto inp = std::make_unique<llm_graph_input_k_shift>(this);
|
|
|
|
inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, cells.size());
|
|
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 defrag_info & dinfo) const {
|
|
auto res = std::make_unique<llm_graph_result>();
|
|
|
|
const auto & ids = dinfo.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;
|
|
}
|
|
|
|
llama_kv_cache_unified::defrag_info llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) const {
|
|
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
|
|
defrag_info res;
|
|
auto & ids = res.ids;
|
|
|
|
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;
|
|
|
|
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 {};
|
|
}
|
|
|
|
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 res;
|
|
}
|
|
|
|
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(true);
|
|
} 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);
|
|
|
|
// 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);
|
|
|
|
// 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);
|
|
|
|
// 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_batch_allocr balloc(hparams.n_pos_per_embd());
|
|
|
|
llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 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 != 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));
|
|
}
|
|
|
|
ubatch.pos[i] = pos;
|
|
ubatch.n_seq_id[i] = n_seq_id;
|
|
ubatch.seq_id[i] = &dest_seq_id;
|
|
}
|
|
|
|
const auto sinfo = find_slot(ubatch, true);
|
|
if (sinfo.empty()) {
|
|
LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
apply_ubatch(sinfo, ubatch);
|
|
|
|
const auto head_cur = sinfo.head();
|
|
|
|
// keep the head at the old position because we will read the KV data into it in state_read_data()
|
|
head = head_cur;
|
|
|
|
// 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) == ubatch.pos[0]);
|
|
GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == ubatch.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(true);
|
|
|
|
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);
|
|
|
|
// 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);
|
|
|
|
// 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);
|
|
|
|
// 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_context
|
|
//
|
|
|
|
llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_status status) : status(status) {}
|
|
|
|
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
|
|
llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
|
|
n_kv = kv->get_size();
|
|
|
|
// create a dummy slot info - the actual data is irrelevant. we just need to build the graph
|
|
sinfos.resize(1);
|
|
sinfos[0].idxs.resize(1);
|
|
sinfos[0].idxs[0] = 0;
|
|
}
|
|
|
|
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
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llama_kv_cache_unified * kv,
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llama_context * lctx,
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bool do_shift,
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defrag_info dinfo) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), lctx(lctx), do_shift(do_shift), dinfo(std::move(dinfo)) {
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if (!do_shift && this->dinfo.empty()) {
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status = LLAMA_MEMORY_STATUS_NO_UPDATE;
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}
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}
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llama_kv_cache_unified_context::llama_kv_cache_unified_context(
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llama_kv_cache_unified * kv,
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llama_kv_cache_unified::slot_info_vec_t sinfos,
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std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sinfos(std::move(sinfos)), ubatches(std::move(ubatches)) {
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}
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llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
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bool llama_kv_cache_unified_context::next() {
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assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
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if (++i_cur >= ubatches.size()) {
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return false;
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}
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return true;
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}
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bool llama_kv_cache_unified_context::apply() {
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assert(!llama_memory_status_is_fail(status));
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// no ubatches -> this is a KV cache update
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if (ubatches.empty()) {
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kv->update(lctx, do_shift, dinfo);
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return true;
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}
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kv->apply_ubatch(sinfos[i_cur], ubatches[i_cur]);
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n_kv = kv->get_n_kv();
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return true;
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}
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llama_memory_status llama_kv_cache_unified_context::get_status() const {
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return status;
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}
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const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
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assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
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return ubatches[i_cur];
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}
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uint32_t llama_kv_cache_unified_context::get_n_kv() const {
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return n_kv;
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}
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ggml_tensor * llama_kv_cache_unified_context::get_k(ggml_context * ctx, int32_t il) const {
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return kv->get_k(ctx, il, n_kv);
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}
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ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t il) const {
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return kv->get_v(ctx, il, n_kv);
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}
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ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
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return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
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}
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ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const {
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return kv->cpy_v(ctx, v_cur, v_idxs, il, sinfos[i_cur]);
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}
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ggml_tensor * llama_kv_cache_unified_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
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return kv->build_input_k_idxs(ctx, ubatch);
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}
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ggml_tensor * llama_kv_cache_unified_context::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
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return kv->build_input_v_idxs(ctx, ubatch);
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}
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void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
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kv->set_input_k_shift(dst);
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}
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void llama_kv_cache_unified_context::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
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kv->set_input_k_idxs(dst, ubatch, sinfos[i_cur]);
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}
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void llama_kv_cache_unified_context::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
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kv->set_input_v_idxs(dst, ubatch, sinfos[i_cur]);
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}
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void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
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kv->set_input_kq_mask(dst, ubatch, causal_attn);
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}
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void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
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kv->set_input_pos_bucket(dst, ubatch);
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}
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uint32_t llama_kv_cache_unified::get_padding(const 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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}
|