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llama: Ensure KV cache is fully defragmented.
Sometimes the KV cache requires defragmentation even without triggering the threshold heuristic. In this case, decoding will not being able to find a KV cache slot. This is particularly difficult for the caller to handle if it happens in between ubatches. To avoid this, we should immediately trigger a defrag. In addition, a heavily fragmented cache can require more than max_moves to defragment. Currently, we stop when we hit the limit but this can leave a cache that still does not have adequate space even after defragmentation is triggered. Instead, we should do multiple batches of processing until everything is complete.
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@ -2955,6 +2955,13 @@ struct llama_kv_cache {
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}
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};
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// block of KV slots to move when defragging
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struct llama_kv_defrag_move {
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uint32_t src;
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uint32_t dst;
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uint32_t len;
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};
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struct llama_control_vector {
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std::vector<struct ggml_tensor *> tensors; // per layer
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std::vector<ggml_context_ptr> ctxs;
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@ -10652,35 +10659,23 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
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struct ggml_cgraph * build_defrag(const std::vector<struct llama_kv_defrag_move> & moves) {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
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for (uint32_t i = 0; i < ids.size(); ++i) {
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const uint32_t id = ids[i];
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if (i == id || id == ids.size()) {
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continue;
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}
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uint32_t nm = 1;
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while (i + nm < ids.size() && ids[i + nm] == id + nm) {
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nm++;
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}
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for (const auto & move : moves) {
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for (int il = 0; il < n_layer; ++il) {
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const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
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const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
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ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
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n_embd_k_gqa, nm,
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n_embd_k_gqa, move.len,
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ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
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ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
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ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*move.src));
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ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
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n_embd_k_gqa, nm,
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n_embd_k_gqa, move.len,
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ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
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ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
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ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*move.dst));
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ggml_tensor * view_v_src;
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ggml_tensor * view_v_dst;
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@ -10688,31 +10683,29 @@ struct llm_build_context {
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if (flash_attn) {
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// NOTE: the V cache is not transposed when using flash attention
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view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
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n_embd_v_gqa, nm,
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n_embd_v_gqa, move.len,
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ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
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ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i));
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ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*move.src));
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view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
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n_embd_v_gqa, nm,
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n_embd_v_gqa, move.len,
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ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
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ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id));
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ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*move.dst));
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} else {
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view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
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nm, n_embd_v_gqa,
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move.len, n_embd_v_gqa,
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ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
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ggml_row_size(kv_self.v_l[il]->type, i));
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ggml_row_size(kv_self.v_l[il]->type, move.src));
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view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
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nm, n_embd_v_gqa,
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move.len, n_embd_v_gqa,
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ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
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ggml_row_size(kv_self.v_l[il]->type, id));
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ggml_row_size(kv_self.v_l[il]->type, move.dst));
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}
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
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}
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i += nm - 1;
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}
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//LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
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@ -16944,7 +16937,7 @@ struct llm_build_context {
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}
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};
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static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
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static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<struct llama_kv_defrag_move> & moves) {
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llama_ubatch dummy = {};
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dummy.equal_seqs = true;
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@ -16954,7 +16947,7 @@ static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const
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llm.init();
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struct ggml_cgraph * result = llm.build_defrag(ids);
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struct ggml_cgraph * result = llm.build_defrag(moves);
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llm.free();
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@ -17957,7 +17950,12 @@ static int llama_decode_internal(
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kv_self.head = 0;
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}
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const auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
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auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
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if (!slot) {
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llama_kv_cache_defrag(kv_self);
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llama_kv_cache_update(&lctx);
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slot = llama_kv_cache_find_slot(kv_self, ubatch);
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}
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if (!slot) {
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return 1;
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}
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@ -18359,8 +18357,8 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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//const int64_t t_start = ggml_time_us();
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// number of cells moved
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uint32_t n_moves = 0;
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// groups of cells moved
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std::vector<struct llama_kv_defrag_move> moves;
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// each move requires 6*n_layer tensors (see build_defrag)
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// - source view, destination view, copy operation
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@ -18424,19 +18422,11 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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// are we moving a continuous block of memory?
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bool cont = false;
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// should we stop searching for the next move?
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bool stop = false;
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// go back and move the nf cells to the hole
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for (; i1 < n_kv; ++i1) {
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auto & cell1 = kv_self.cells[i1];
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if (cell1.is_empty() || ids[i1] != n_kv) {
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if (n_moves == max_moves) {
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stop = true;
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break;
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}
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cont = false;
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continue;
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}
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@ -18452,8 +18442,10 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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kv_self.head = n_used;
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if (!cont) {
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n_moves++;
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moves.push_back({i1, i0 + nf, 1});
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cont = true;
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} else {
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moves.back().len++;
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}
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nf++;
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@ -18463,22 +18455,16 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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}
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}
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if (stop || n_moves == max_moves) {
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break;
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}
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//LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
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i0 += nh - 1;
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}
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if (n_moves == 0) {
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if (moves.size() == 0) {
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return;
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}
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//LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
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//LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
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//LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", moves.size());
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#if 0
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// CPU defrag
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@ -18553,11 +18539,18 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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#else
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// ggml_graph defrag
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for (std::size_t i = 0; i < moves.size(); i += max_moves) {
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std::vector<struct llama_kv_defrag_move> chunk;
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auto end = std::min(i + max_moves, moves.size());
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chunk.assign(moves.begin() + i, moves.begin() + end);
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ggml_backend_sched_reset(lctx.sched.get());
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ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
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//LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*chunk.size()*n_layer);
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ggml_cgraph * gf = llama_build_graph_defrag(lctx, chunk);
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llama_graph_compute(lctx, gf, lctx.cparams.n_threads, lctx.threadpool);
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}
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#endif
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//const int64_t t_end = ggml_time_us();
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