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+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
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+From: Jesse Gross <jesse@ollama.com>
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+Date: Fri, 13 Dec 2024 16:11:59 -0800
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+Subject: [PATCH] llama: Ensure KV cache is fully defragmented.
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+
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+Sometimes the KV cache requires defragmentation even without
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+triggering the threshold heuristic. In this case, decoding
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+will not being able to find a KV cache slot. This is particularly
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+difficult for the caller to handle if it happens in between
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+ubatches. To avoid this, we should immediately trigger a defrag.
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+
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+In addition, a heavily fragmented cache can require more than
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+max_moves to defragment. Currently, we stop when we hit the limit
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+but this can leave a cache that still does not have adequate space
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+even after defragmentation is triggered. Instead, we should do
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+multiple batches of processing until everything is complete.
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+---
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+ src/llama.cpp | 99 ++++++++++++++++++++++++---------------------------
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+ 1 file changed, 46 insertions(+), 53 deletions(-)
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+
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+diff --git a/src/llama.cpp b/src/llama.cpp
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+index 4778a9ed..654e32bc 100644
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+--- a/src/llama.cpp
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++++ b/src/llama.cpp
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+@@ -3025,6 +3025,13 @@ struct llama_kv_cache {
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+ }
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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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++
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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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+@@ -10802,35 +10809,23 @@ struct llm_build_context {
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+ return gf;
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+ }
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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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+
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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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+-
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+- if (i == id || id == ids.size()) {
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+- continue;
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+- }
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+-
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+- uint32_t nm = 1;
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+-
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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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+-
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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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+
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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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+
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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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+
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+ ggml_tensor * view_v_src;
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+ ggml_tensor * view_v_dst;
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+@@ -10838,31 +10833,29 @@ 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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+
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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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+
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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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+
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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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+-
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+- i += nm - 1;
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+ }
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+
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+ //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
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+@@ -17325,7 +17318,7 @@ struct llm_build_context {
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+ }
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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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+
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+@@ -17335,7 +17328,7 @@ static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const
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+
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+ llm.init();
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+
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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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+
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+ llm.free();
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+
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+@@ -18351,7 +18344,12 @@ static int llama_decode_internal(
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+ kv_self.head = 0;
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+ }
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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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+@@ -18756,8 +18754,8 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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+
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+ //const int64_t t_start = ggml_time_us();
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+
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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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+
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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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+@@ -18821,19 +18819,11 @@ 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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+
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+- // should we stop searching for the next move?
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+- bool stop = false;
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+-
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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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+
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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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+-
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+ cont = false;
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+ continue;
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+ }
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+@@ -18849,8 +18839,10 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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+ kv_self.head = n_used;
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+
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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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+
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+ nf++;
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+@@ -18860,22 +18852,16 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
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+ }
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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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+-
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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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+
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+ i0 += nh - 1;
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+ }
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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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+
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+- //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
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+-
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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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+
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+ #if 0
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+ // CPU defrag
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+@@ -18950,11 +18936,18 @@ 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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+
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+- ggml_backend_sched_reset(lctx.sched.get());
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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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+
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+- ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
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++ ggml_backend_sched_reset(lctx.sched.get());
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++
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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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+
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+- llama_graph_compute(lctx, gf, lctx.cparams.n_threads, lctx.threadpool);
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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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+
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+ //const int64_t t_end = ggml_time_us();
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