kv-cache : simplify the interface (#13660)

* kv-cache : simplify the interface

ggml-ci

* context : revert llama_batch_allocr position change

ggml-ci

examples/simple-chat/simple-chat.cpp

@@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
     auto generate = [&](const std::string & prompt) {
         std::string response;
 
-        const bool is_first = llama_kv_self_used_cells(ctx) == 0;
+        const bool is_first = llama_kv_self_seq_pos_max(ctx, 0) == 0;
 
         // tokenize the prompt
         const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
@@ -113,7 +113,7 @@ int main(int argc, char ** argv) {
         while (true) {
             // check if we have enough space in the context to evaluate this batch
             int n_ctx = llama_n_ctx(ctx);
-            int n_ctx_used = llama_kv_self_used_cells(ctx);
+            int n_ctx_used = llama_kv_self_seq_pos_max(ctx, 0);
             if (n_ctx_used + batch.n_tokens > n_ctx) {
                 printf("\033[0m\n");
                 fprintf(stderr, "context size exceeded\n");

include/llama.h

@@ -610,10 +610,12 @@ extern "C" {
 
     // Returns the number of tokens in the KV cache (slow, use only for debug)
     // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
-    LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx);
+    DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx),
+               "Use llama_kv_self_seq_pos_max() instead");
 
     // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
-    LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx);
+    DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx),
+               "Use llama_kv_self_seq_pos_max() instead");
 
     // Clear the KV cache - both cell info is erased and KV data is zeroed
     LLAMA_API void llama_kv_self_clear(

src/llama-batch.cpp

@@ -1,5 +1,6 @@
 #include "llama-batch.h"
 
+#include <cassert>
 #include <cstring>
 #include <algorithm>
 
@@ -281,9 +282,10 @@ llama_batch_allocr::llama_batch_allocr(struct llama_batch in_batch, llama_pos p0
     batch = in_batch;
     GGML_ASSERT(batch.n_tokens > 0);
     if (!batch.pos) {
+        assert(p0 >= 0);
         pos.resize(batch.n_tokens);
         for (int32_t i = 0; i < batch.n_tokens; i++) {
-            pos[i] = i + p0;
+            pos[i] = p0 + i;
         }
         batch.pos = pos.data();
     }

src/llama-context.cpp

@@ -857,11 +857,17 @@ int llama_context::decode(llama_batch & inp_batch) {
         return -1;
     }
 
+    if (!inp_batch.pos) {
+        if (inp_batch.seq_id) {
+            LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__);
+            return -1;
+        }
+    }
+
     llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
 
     // temporary allocate memory for the input batch if needed
-    // TODO: this is incorrect for multiple sequences because get_pos_max() is the maximum across all sequences
-    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->get_pos_max() + 1);
+    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->seq_pos_max(0) + 1);
 
     const llama_batch & batch = batch_allocr.batch;
 
@@ -2292,22 +2298,47 @@ int32_t llama_apply_adapter_cvec(
 // kv cache
 //
 
+// deprecated
 int32_t llama_kv_self_n_tokens(const llama_context * ctx) {
     const auto * kv = ctx->get_kv_self();
     if (!kv) {
         return 0;
     }
 
-    return kv->get_n_tokens();
+    int32_t res = 0;
+
+    for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
+        const llama_pos p0 = kv->seq_pos_min(s);
+        const llama_pos p1 = kv->seq_pos_max(s);
+
+        if (p0 >= 0) {
+            res += (p1 - p0) + 1;
+        }
+    }
+
+    return res;
 }
 
+// deprecated
+// note: this is the same as above - will be removed anyway, so it's ok
 int32_t llama_kv_self_used_cells(const llama_context * ctx) {
     const auto * kv = ctx->get_kv_self();
     if (!kv) {
         return 0;
     }
 
-    return kv->get_used_cells();
+    int32_t res = 0;
+
+    for (uint32_t s = 0; s < ctx->get_cparams().n_seq_max; s++) {
+        const llama_pos p0 = kv->seq_pos_min(s);
+        const llama_pos p1 = kv->seq_pos_max(s);
+
+        if (p0 >= 0) {
+            res += (p1 - p0) + 1;
+        }
+    }
+
+    return res;
 }
 
 void llama_kv_self_clear(llama_context * ctx) {

src/llama-kv-cache.cpp

@@ -30,13 +30,14 @@ llama_kv_cache_unified::llama_kv_cache_unified(
                      bool    v_trans,
                      bool    offload,
                  uint32_t    kv_size,
-                 uint32_t    padding,
+                 uint32_t    n_seq_max,
+                 uint32_t    n_pad,
                  uint32_t    n_swa,
-           llama_swa_type    swa_type) : model(model), hparams(model.hparams), v_trans(v_trans), padding(padding), n_swa(n_swa), swa_type(swa_type) {
-    GGML_ASSERT(kv_size % padding == 0 && "kv_size must be a multiple of padding");
+           llama_swa_type    swa_type) :
+    model(model), hparams(model.hparams), v_trans(v_trans),
+    n_seq_max(n_seq_max), n_pad(n_pad), n_swa(n_swa), swa_type(swa_type) {
 
-    this->type_k = type_k;
-    this->type_v = type_v;
+    GGML_ASSERT(kv_size % n_pad == 0);
 
     // create a context for each buffer type
     std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
@@ -129,8 +130,8 @@ llama_kv_cache_unified::llama_kv_cache_unified(
         const size_t memory_size_k = size_k_bytes();
         const size_t memory_size_v = size_v_bytes();
 
-        LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6d cells, %3d layers), K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
-                (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(),
+        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__,
+                (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), kv_size, (int) layers.size(), n_seq_max,
                 ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
                 ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
     }
@@ -442,7 +443,7 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
 void llama_kv_cache_unified::defrag_sched(float thold) {
     // - do not defrag small contexts (i.e. < 2048 tokens)
     // - count the padding towards the number of used tokens
-    const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + padding)/n)) : 0.0f;
+    const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + n_pad)/n)) : 0.0f;
 
     // queue defragmentation for next llama_kv_cache_update
     if (fragmentation > thold) {
@@ -558,7 +559,7 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
     // a heuristic, to avoid attending the full cache if it is not yet utilized
     // after enough generations, the benefit from this heuristic disappears
     // if we start defragmenting the cache, the benefit from this will be more important
-    n = std::min(size, std::max(padding, GGML_PAD(cell_max(), padding)));
+    n = std::min(size, std::max(n_pad, GGML_PAD(cell_max(), n_pad)));
 
 #ifdef FIND_SLOT_DEBUG
     LLAMA_LOG_WARN("end:   n = %5d, used = %5d, head = %5d, n_swa = %5d\n", n, used, head, n_swa);
@@ -567,20 +568,6 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
     return true;
 }
 
-int32_t llama_kv_cache_unified::get_n_tokens() const {
-    int32_t result = 0;
-
-    for (uint32_t i = 0; i < size; i++) {
-        result += cells[i].seq_id.size();
-    }
-
-    return result;
-}
-
-int32_t llama_kv_cache_unified::get_used_cells() const {
-    return used;
-}
-
 bool llama_kv_cache_unified::get_can_shift() const {
     return true;
 }
@@ -802,16 +789,6 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
     }
 }
 
-llama_pos llama_kv_cache_unified::get_pos_max() const {
-    llama_pos pos_max = -1;
-
-    for (const auto & cell : cells) {
-        pos_max = std::max(pos_max, cell.pos);
-    }
-
-    return pos_max;
-}
-
 size_t llama_kv_cache_unified::total_size() const {
     size_t size = 0;
 
@@ -1501,11 +1478,8 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
                 llama_seq_id seq_id;
                 io.read_to(&seq_id, sizeof(seq_id));
 
-                // TODO: llama_kv_cache_unified should have a notion of max sequences
-                //if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) {
-                if (seq_id < 0) {
-                    //LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx));
-                    LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, inf)\n", __func__, 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;
                 }
 
@@ -1655,17 +1629,17 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
                 ggml_type   type_v,
                      bool   v_trans,
                      bool   offload,
-                 uint32_t   kv_size,
                      bool   swa_full,
+                 uint32_t   kv_size,
                  uint32_t   n_seq_max,
                  uint32_t   n_batch,
-                 uint32_t   padding) : hparams(model.hparams) {
+                 uint32_t   n_pad) : hparams(model.hparams) {
     llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); };
     llama_kv_cache_unified::layer_filter_cb filter_swa  = [&](int32_t il) { return  model.hparams.is_swa(il); };
 
     const uint32_t size_base = kv_size;
 
-    uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, padding));
+    uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_batch, n_pad));
 
     // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size and disable pruning
     if (swa_full) {
@@ -1680,14 +1654,14 @@ llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa(
 
     kv_base = std::make_unique<llama_kv_cache_unified>(
             model, std::move(filter_base), type_k, type_v,
-            v_trans, offload, size_base, padding,
+            v_trans, offload, size_base, n_seq_max, n_pad,
             0, LLAMA_SWA_TYPE_NONE);
 
     LLAMA_LOG_INFO("%s: creating     SWA KV cache, size = %u cells\n", __func__, size_swa);
 
     kv_swa = std::make_unique<llama_kv_cache_unified>(
             model, std::move(filter_swa), type_k, type_v,
-            v_trans, offload, size_swa, padding,
+            v_trans, offload, size_swa, n_seq_max, n_pad,
             hparams.n_swa, hparams.swa_type);
 }
 
@@ -1810,18 +1784,6 @@ bool llama_kv_cache_unified_iswa::find_slot(const llama_ubatch & batch) {
     return res;
 }
 
-int32_t llama_kv_cache_unified_iswa::get_n_tokens()   const {
-    return kv_base->get_n_tokens();
-}
-
-int32_t llama_kv_cache_unified_iswa::get_used_cells() const {
-    return kv_base->get_used_cells();
-}
-
-llama_pos llama_kv_cache_unified_iswa::get_pos_max() const {
-    return kv_base->get_pos_max();
-}
-
 bool llama_kv_cache_unified_iswa::get_can_shift() const {
     return kv_base->get_size() == kv_swa->get_size();
 }
@@ -1853,19 +1815,17 @@ llama_kv_cache_recurrent::llama_kv_cache_recurrent(
                 ggml_type   type_k,
                 ggml_type   type_v,
                      bool   offload,
-                 uint32_t   kv_size) : hparams(model.hparams) {
+                 uint32_t   kv_size,
+                 uint32_t   n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
     const int32_t n_layer = hparams.n_layer;
 
-    LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d\n",
-            __func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
+    LLAMA_LOG_INFO("%s: kv_size = %u, n_seq_max = %u, type_k = '%s', type_v = '%s', n_layer = %d\n",
+            __func__, kv_size, n_seq_max, ggml_type_name(type_k), ggml_type_name(type_v), n_layer);
 
     head = 0;
     size = kv_size;
     used = 0;
 
-    this->type_k = type_k;
-    this->type_v = type_v;
-
     cells.clear();
     cells.resize(kv_size);
 
@@ -2203,8 +2163,8 @@ void llama_kv_cache_recurrent::commit() {
     pending.ranges.clear();
 }
 
-bool llama_kv_cache_recurrent::update(llama_context & lctx) {
-    GGML_UNUSED(lctx);
+bool llama_kv_cache_recurrent::update(llama_context & ctx) {
+    GGML_UNUSED(ctx);
     return false;
 }
 
@@ -2265,7 +2225,7 @@ bool llama_kv_cache_recurrent::find_slot(
             if (seq_id < 0 || (uint32_t) seq_id >= size) {
                 // too big seq_id
                 // TODO: would it be possible to resize the cache instead?
-                LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size);
+                LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%u Try using a bigger --parallel value\n", __func__, seq_id, n_seq_max);
                 return false;
             }
             if (j > 0) {
@@ -2408,29 +2368,6 @@ bool llama_kv_cache_recurrent::find_slot(
     return n >= n_seqs;
 }
 
-int32_t llama_kv_cache_recurrent::get_n_tokens() const {
-    int32_t result = 0;
-
-    for (uint32_t i = 0; i < size; i++) {
-        result += cells[i].seq_id.size();
-    }
-
-    return result;
-}
-
-int32_t llama_kv_cache_recurrent::get_used_cells() const {
-    return used;
-}
-
-llama_pos llama_kv_cache_recurrent::get_pos_max() const {
-    llama_pos pos_max = -1;
-    for (const auto & cell : cells) {
-        pos_max = std::max(pos_max, cell.pos);
-    }
-
-    return pos_max;
-}
-
 bool llama_kv_cache_recurrent::get_can_shift() const {
     return false;
 }

src/llama-kv-cache.h

@@ -55,10 +55,7 @@ struct llama_kv_cache : public llama_memory_i {
     // =============================================================================================================
 
     // getters
-    virtual int32_t   get_n_tokens()   const = 0;
-    virtual int32_t   get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache
-    virtual llama_pos get_pos_max()    const = 0;
-    virtual bool      get_can_shift()  const = 0;
+    virtual bool get_can_shift() const = 0;
 
     bool get_can_edit() const override { return get_can_shift(); }
 
@@ -108,7 +105,8 @@ public:
                          bool    v_trans,
                          bool    offload,
                      uint32_t    kv_size,
-                     uint32_t    padding,
+                     uint32_t    n_seq_max,
+                     uint32_t    n_pad,
                      uint32_t    n_swa,
                llama_swa_type    swa_type);
 
@@ -150,12 +148,6 @@ public:
     // to the first cell of the slot.
     bool find_slot(const llama_ubatch & batch) override;
 
-    int32_t get_n_tokens()   const override;
-    int32_t get_used_cells() const override;
-
-    // TODO: better data structures to reduce the cost of this operation
-    llama_pos get_pos_max() const override;
-
     bool get_can_shift() const override;
 
     // state write/load
@@ -228,16 +220,15 @@ private:
     // computed before each graph build
     uint32_t n = 0;
 
-    // required padding
-    uint32_t padding = 1;
+    const uint32_t n_seq_max = 1;
 
-    ggml_type type_k = GGML_TYPE_F16;
-    ggml_type type_v = GGML_TYPE_F16;
+    // required padding
+    const uint32_t n_pad = 1;
 
     // SWA
-    uint32_t n_swa = 0;
+    const uint32_t n_swa = 0;
 
-    llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
+    const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
 
     std::vector<ggml_context_ptr>        ctxs;
     std::vector<ggml_backend_buffer_ptr> bufs;
@@ -317,11 +308,11 @@ public:
                     ggml_type   type_v,
                          bool   v_trans,
                          bool   offload,
-                     uint32_t   kv_size,
                          bool   swa_full,
+                     uint32_t   kv_size,
                      uint32_t   n_seq_max,
                      uint32_t   n_batch,
-                     uint32_t   padding);
+                     uint32_t   n_pad);
 
     ~llama_kv_cache_unified_iswa() = default;
 
@@ -358,12 +349,6 @@ public:
 
     bool find_slot(const llama_ubatch & batch) override;
 
-    int32_t get_n_tokens()   const override;
-    int32_t get_used_cells() const override;
-
-    // TODO: better data structures to reduce the cost of this operation
-    llama_pos get_pos_max() const override;
-
     bool get_can_shift() const override;
 
     // state write/load
@@ -432,7 +417,8 @@ public:
                     ggml_type   type_k,
                     ggml_type   type_v,
                          bool   offload,
-                     uint32_t   kv_size);
+                     uint32_t   kv_size,
+                     uint32_t   n_seq_max);
 
     ~llama_kv_cache_recurrent() = default;
 
@@ -444,7 +430,7 @@ public:
 
     bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
     void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
-    void seq_keep(llama_seq_id seq_id) override;
+    void seq_keep(llama_seq_id seq_id)                                                          override;
     void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) override;
     void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
 
@@ -458,7 +444,7 @@ public:
     void restore() override;
     void commit()  override;
 
-    bool update(llama_context & lctx) override;
+    bool update(llama_context & ctx) override;
 
     void defrag_sched(float thold) override;
 
@@ -469,12 +455,6 @@ public:
 
     bool find_slot(const llama_ubatch & batch) override;
 
-    int32_t get_n_tokens()   const override;
-    int32_t get_used_cells() const override;
-
-    // TODO: better data structures to reduce the cost of this operation
-    llama_pos get_pos_max() const override;
-
     bool get_can_shift() const override;
 
     // TODO: temporary methods - they are not really const as they do const_cast<>, fix this
@@ -514,8 +494,7 @@ private:
         std::vector<slot_range> ranges;
     } pending;
 
-    ggml_type type_k = GGML_TYPE_F16;
-    ggml_type type_v = GGML_TYPE_F16;
+    const uint32_t n_seq_max = 1;
 
     std::vector<ggml_context_ptr>        ctxs;
     std::vector<ggml_backend_buffer_ptr> bufs;

src/llama-model.cpp

@@ -13203,7 +13203,8 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         GGML_TYPE_F32,
                         GGML_TYPE_F32,
                         cparams.offload_kqv,
-                        std::max((uint32_t) 1, cparams.n_seq_max));
+                        std::max((uint32_t) 1, cparams.n_seq_max),
+                        cparams.n_seq_max);
             } break;
         default:
             {
@@ -13222,8 +13223,8 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             params.type_v,
                             !cparams.flash_attn,
                             cparams.offload_kqv,
-                            cparams.n_ctx,
                             params.swa_full,
+                            cparams.n_ctx,
                             cparams.n_seq_max,
                             cparams.n_batch,
                             padding);
@@ -13238,6 +13239,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             !cparams.flash_attn,
                             cparams.offload_kqv,
                             cparams.n_ctx,
+                            cparams.n_seq_max,
                             padding,
                             hparams.n_swa,
                             hparams.swa_type);

tools/run/run.cpp

@@ -936,7 +936,7 @@ static int apply_chat_template(const struct common_chat_templates * tmpls, Llama
 // Function to tokenize the prompt
 static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
                            std::vector<llama_token> & prompt_tokens, const LlamaData & llama_data) {
-    const bool is_first = llama_kv_self_used_cells(llama_data.context.get()) == 0;
+    const bool is_first = llama_kv_self_seq_pos_max(llama_data.context.get(), 0) == 0;
 
     const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
     prompt_tokens.resize(n_prompt_tokens);
@@ -952,7 +952,7 @@ static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt
 // Check if we have enough space in the context to evaluate this batch
 static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) {
     const int n_ctx      = llama_n_ctx(ctx.get());
-    const int n_ctx_used = llama_kv_self_used_cells(ctx.get());
+    const int n_ctx_used = llama_kv_self_seq_pos_max(ctx.get(), 0);
     if (n_ctx_used + batch.n_tokens > n_ctx) {
         printf(LOG_COL_DEFAULT "\n");
         printe("context size exceeded\n");

tools/server/server.cpp

@@ -1137,9 +1137,6 @@ struct server_task_result_metrics : server_task_result {
     int n_tasks_deferred;
     int64_t t_start;
 
-    int32_t kv_cache_tokens_count;
-    int32_t kv_cache_used_cells;
-
     // TODO: somehow reuse server_metrics in the future, instead of duplicating the fields
     uint64_t n_prompt_tokens_processed_total = 0;
     uint64_t t_prompt_processing_total       = 0;
@@ -1179,9 +1176,6 @@ struct server_task_result_metrics : server_task_result {
             { "n_decode_total",                  n_decode_total },
             { "n_busy_slots_total",              n_busy_slots_total },
 
-            { "kv_cache_tokens_count",           kv_cache_tokens_count },
-            { "kv_cache_used_cells",             kv_cache_used_cells },
-
             { "slots",                           slots_data },
         };
     }
@@ -2771,9 +2765,6 @@ struct server_context {
                     res->n_tasks_deferred    = queue_tasks.queue_tasks_deferred.size();
                     res->t_start             = metrics.t_start;
 
-                    res->kv_cache_tokens_count = llama_kv_self_n_tokens(ctx);
-                    res->kv_cache_used_cells   = llama_kv_self_used_cells(ctx);
-
                     res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total;
                     res->t_prompt_processing_total       = metrics.t_prompt_processing_total;
                     res->n_tokens_predicted_total        = metrics.n_tokens_predicted_total;
@@ -3883,14 +3874,6 @@ int main(int argc, char ** argv) {
                     {"name",  "predicted_tokens_seconds"},
                     {"help",  "Average generation throughput in tokens/s."},
                     {"value",  res_metrics->n_tokens_predicted ? 1.e3 / res_metrics->t_tokens_generation * res_metrics->n_tokens_predicted : 0.}
-            },{
-                    {"name",  "kv_cache_usage_ratio"},
-                    {"help",  "KV-cache usage. 1 means 100 percent usage."},
-                    {"value",  1. * res_metrics->kv_cache_used_cells / params.n_ctx}
-            },{
-                    {"name",  "kv_cache_tokens"},
-                    {"help",  "KV-cache tokens."},
-                    {"value",  (uint64_t) res_metrics->kv_cache_tokens_count}
             },{
                     {"name",  "requests_processing"},
                     {"help",  "Number of requests processing."},