From a2d4b6fc818fe8503a28ac8cac33cb622f1d7d3b Mon Sep 17 00:00:00 2001 From: Jesse Gross Date: Fri, 13 Dec 2024 16:11:59 -0800 Subject: [PATCH] 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. --- src/llama.cpp | 99 ++++++++++++++++++++++++--------------------------- 1 file changed, 46 insertions(+), 53 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 1cc8a9332..757cb0124 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2955,6 +2955,13 @@ struct llama_kv_cache { } }; +// block of KV slots to move when defragging +struct llama_kv_defrag_move { + uint32_t src; + uint32_t dst; + uint32_t len; +}; + struct llama_control_vector { std::vector tensors; // per layer std::vector ctxs; @@ -10652,35 +10659,23 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_defrag(const std::vector & ids) { + struct ggml_cgraph * build_defrag(const std::vector & moves) { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); - 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 & move : moves) { for (int il = 0; il < n_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(ctx0, kv_self.k_l[il], - n_embd_k_gqa, nm, + n_embd_k_gqa, move.len, ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i)); + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*move.src)); ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il], - n_embd_k_gqa, nm, + n_embd_k_gqa, move.len, ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id)); + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*move.dst)); ggml_tensor * view_v_src; ggml_tensor * view_v_dst; @@ -10688,31 +10683,29 @@ struct llm_build_context { if (flash_attn) { // NOTE: the V cache is not transposed when using flash attention view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il], - n_embd_v_gqa, nm, + n_embd_v_gqa, move.len, ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa), - ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i)); + ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*move.src)); view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il], - n_embd_v_gqa, nm, + n_embd_v_gqa, move.len, ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa), - ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id)); + ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*move.dst)); } else { view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il], - nm, n_embd_v_gqa, + move.len, n_embd_v_gqa, ggml_row_size(kv_self.v_l[il]->type, kv_self.size), - ggml_row_size(kv_self.v_l[il]->type, i)); + ggml_row_size(kv_self.v_l[il]->type, move.src)); view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il], - nm, n_embd_v_gqa, + move.len, n_embd_v_gqa, ggml_row_size(kv_self.v_l[il]->type, kv_self.size), - ggml_row_size(kv_self.v_l[il]->type, id)); + ggml_row_size(kv_self.v_l[il]->type, move.dst)); } ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst)); ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst)); } - - i += nm - 1; } //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes); @@ -16944,7 +16937,7 @@ struct llm_build_context { } }; -static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { +static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & moves) { llama_ubatch dummy = {}; dummy.equal_seqs = true; @@ -16954,7 +16947,7 @@ static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const llm.init(); - struct ggml_cgraph * result = llm.build_defrag(ids); + struct ggml_cgraph * result = llm.build_defrag(moves); llm.free(); @@ -17957,7 +17950,12 @@ static int llama_decode_internal( kv_self.head = 0; } - const auto slot = llama_kv_cache_find_slot(kv_self, ubatch); + auto slot = llama_kv_cache_find_slot(kv_self, ubatch); + if (!slot) { + llama_kv_cache_defrag(kv_self); + llama_kv_cache_update(&lctx); + slot = llama_kv_cache_find_slot(kv_self, ubatch); + } if (!slot) { return 1; } @@ -18359,8 +18357,8 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { //const int64_t t_start = ggml_time_us(); - // number of cells moved - uint32_t n_moves = 0; + // groups of cells moved + std::vector moves; // each move requires 6*n_layer tensors (see build_defrag) // - source view, destination view, copy operation @@ -18424,19 +18422,11 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // 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) { auto & cell1 = kv_self.cells[i1]; if (cell1.is_empty() || ids[i1] != n_kv) { - if (n_moves == max_moves) { - stop = true; - break; - } - cont = false; continue; } @@ -18452,8 +18442,10 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { kv_self.head = n_used; if (!cont) { - n_moves++; + moves.push_back({i1, i0 + nf, 1}); cont = true; + } else { + moves.back().len++; } nf++; @@ -18463,22 +18455,16 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { } } - 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) { + if (moves.size() == 0) { return; } - //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves); - - //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer); + //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", moves.size()); #if 0 // CPU defrag @@ -18553,11 +18539,18 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { #else // ggml_graph defrag - ggml_backend_sched_reset(lctx.sched.get()); + for (std::size_t i = 0; i < moves.size(); i += max_moves) { + std::vector chunk; + auto end = std::min(i + max_moves, moves.size()); + chunk.assign(moves.begin() + i, moves.begin() + end); - ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids); + ggml_backend_sched_reset(lctx.sched.get()); - llama_graph_compute(lctx, gf, lctx.cparams.n_threads, lctx.threadpool); + //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*chunk.size()*n_layer); + ggml_cgraph * gf = llama_build_graph_defrag(lctx, chunk); + + llama_graph_compute(lctx, gf, lctx.cparams.n_threads, lctx.threadpool); + } #endif //const int64_t t_end = ggml_time_us();