diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index d12d63244..25ab044dc 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -281,8 +281,12 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
-    if (self_kv_idxs) {
-        mctx->set_input_kv_idxs(self_kv_idxs, ubatch);
+    if (self_k_idxs) {
+        mctx->set_input_k_idxs(self_k_idxs, ubatch);
+    }
+
+    if (self_v_idxs) {
+        mctx->set_input_v_idxs(self_v_idxs, ubatch);
     }
 
     if (self_kq_mask) {
@@ -291,12 +295,20 @@ void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
-    if (self_kv_idxs) {
-        mctx->get_base()->set_input_kv_idxs(self_kv_idxs, ubatch);
+    if (self_k_idxs) {
+        mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
     }
 
-    if (self_kv_idxs_swa) {
-        mctx->get_swa()->set_input_kv_idxs(self_kv_idxs_swa, ubatch);
+    if (self_v_idxs) {
+        mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
+    }
+
+    if (self_k_idxs_swa) {
+        mctx->get_swa()->set_input_k_idxs(self_k_idxs_swa, ubatch);
+    }
+
+    if (self_v_idxs_swa) {
+        mctx->get_swa()->set_input_v_idxs(self_v_idxs_swa, ubatch);
     }
 
     if (self_kq_mask) {
@@ -345,6 +357,14 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
+    if (self_k_idxs) {
+        mctx->get_attn()->set_input_k_idxs(self_k_idxs, ubatch);
+    }
+
+    if (self_v_idxs) {
+        mctx->get_attn()->set_input_v_idxs(self_v_idxs, ubatch);
+    }
+
     if (self_kq_mask) {
         mctx->get_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
     }
@@ -362,7 +382,8 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
     }
 }
 
-void llm_graph_input_one::set_input(const llama_ubatch *) {
+void llm_graph_input_one::set_input(const llama_ubatch * ubatch) {
+    GGML_UNUSED(ubatch);
     GGML_ASSERT(one && ggml_nelements(one) == 1);
     float f_one = 1.0f;
     ggml_backend_tensor_set(one, &f_one, 0, sizeof(float));
@@ -1009,6 +1030,9 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
 
         const auto n_kv = inp->mctx->get_attn()->get_n_kv();
 
+        inp->self_k_idxs = mctx_cur->get_attn()->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs = mctx_cur->get_attn()->build_input_v_idxs(ctx0, ubatch);
+
         inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
         //cb(inp->self_kq_mask, "KQ_mask", -1);
         ggml_set_input(inp->self_kq_mask);
@@ -1210,11 +1234,10 @@ llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified()
 
         const auto n_kv = mctx_cur->get_n_kv();
 
-        inp->self_kv_idxs = ggml_new_tensor_1d(ctx0, GGML_TYPE_I64, n_tokens);
-        ggml_set_input(inp->self_kv_idxs);
+        inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs = mctx_cur->build_input_v_idxs(ctx0, ubatch);
 
         inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask, "KQ_mask", -1);
         ggml_set_input(inp->self_kq_mask);
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
@@ -1245,10 +1268,11 @@ ggml_tensor * llm_graph_context::build_attn(
 
     // store to KV cache
     {
-        const auto & kv_idxs = inp->get_kv_idxs();
+        const auto & k_idxs = inp->get_k_idxs();
+        const auto & v_idxs = inp->get_v_idxs();
 
-        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, kv_idxs, il));
-        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, kv_idxs, il));
+        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
+        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
     }
 
     const auto & kq_mask = inp->get_kq_mask();
@@ -1307,15 +1331,15 @@ ggml_tensor * llm_graph_context::build_attn(
 
     // optionally store to KV cache
     if (k_cur) {
-        const auto & kv_idxs = is_swa ? inp->get_kv_idxs_swa() : inp->get_kv_idxs();
+        const auto & k_idxs = is_swa ? inp->get_k_idxs_swa() : inp->get_k_idxs();
 
-        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, kv_idxs, il));
+        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
     }
 
     if (v_cur) {
-        const auto & kv_idxs = is_swa ? inp->get_kv_idxs_swa() : inp->get_kv_idxs();
+        const auto & v_idxs = is_swa ? inp->get_v_idxs_swa() : inp->get_v_idxs();
 
-        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, kv_idxs, il));
+        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
     }
 
     const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
@@ -1419,8 +1443,11 @@ ggml_tensor * llm_graph_context::build_attn(
 
     // store to KV cache
     {
-        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, nullptr, il));
-        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, nullptr, il));
+        const auto & k_idxs = inp->get_k_idxs();
+        const auto & v_idxs = inp->get_v_idxs();
+
+        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
+        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
     }
 
     const auto & kq_mask = inp->get_kq_mask();
@@ -1455,11 +1482,10 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
     {
         const auto n_kv = mctx_cur->get_base()->get_n_kv();
 
-        inp->self_kv_idxs = ggml_new_tensor_1d(ctx0, GGML_TYPE_I64, n_tokens);
-        ggml_set_input(inp->self_kv_idxs);
+        inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs = mctx_cur->get_base()->build_input_v_idxs(ctx0, ubatch);
 
         inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask, "KQ_mask", -1);
         ggml_set_input(inp->self_kq_mask);
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
@@ -1470,11 +1496,10 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
 
         const auto n_kv = mctx_cur->get_swa()->get_n_kv();
 
-        inp->self_kv_idxs_swa = ggml_new_tensor_1d(ctx0, GGML_TYPE_I64, n_tokens);
-        ggml_set_input(inp->self_kv_idxs_swa);
+        inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs_swa = mctx_cur->get_swa()->build_input_v_idxs(ctx0, ubatch);
 
         inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
         ggml_set_input(inp->self_kq_mask_swa);
 
         inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 49c8828cb..769cb6938 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -248,10 +248,13 @@ public:
 
     void set_input(const llama_ubatch * ubatch) override;
 
-    ggml_tensor * get_kv_idxs() const { return self_kv_idxs; }
+    ggml_tensor * get_k_idxs() const { return self_k_idxs; }
+    ggml_tensor * get_v_idxs() const { return self_v_idxs; }
+
     ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
 
-    ggml_tensor * self_kv_idxs = nullptr; // I64 [n_batch]
+    ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs = nullptr; // I64 [n_batch]
 
     ggml_tensor * self_kq_mask     = nullptr; // F32 [n_kv, n_batch]
     ggml_tensor * self_kq_mask_cnv = nullptr; //     [n_kv, n_batch]
@@ -276,13 +279,18 @@ public:
 
     void set_input(const llama_ubatch * ubatch) override;
 
-    ggml_tensor * get_kv_idxs()     const { return self_kv_idxs; }
-    ggml_tensor * get_kv_idxs_swa() const { return self_kv_idxs_swa; }
+    ggml_tensor * get_k_idxs()     const { return self_k_idxs; }
+    ggml_tensor * get_v_idxs()     const { return self_v_idxs; }
+    ggml_tensor * get_k_idxs_swa() const { return self_k_idxs_swa; }
+    ggml_tensor * get_v_idxs_swa() const { return self_v_idxs_swa; }
+
     ggml_tensor * get_kq_mask()     const { return self_kq_mask_cnv; }
     ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; }
 
-    ggml_tensor * self_kv_idxs     = nullptr; // I64 [n_batch]
-    ggml_tensor * self_kv_idxs_swa = nullptr; // I64 [n_batch]
+    ggml_tensor * self_k_idxs     = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs     = nullptr; // I64 [n_batch]
+    ggml_tensor * self_k_idxs_swa = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs_swa = nullptr; // I64 [n_batch]
 
     ggml_tensor * self_kq_mask         = nullptr; // F32 [n_kv, n_batch]
     ggml_tensor * self_kq_mask_cnv     = nullptr; //     [n_kv, n_batch]
@@ -326,8 +334,14 @@ public:
 
     ggml_tensor * s_copy; // I32 [kv_size]
 
+    ggml_tensor * get_k_idxs() const { return self_k_idxs; }
+    ggml_tensor * get_v_idxs() const { return self_v_idxs; }
+
     ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
 
+    ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs = nullptr; // I64 [n_batch]
+
     ggml_tensor * self_kq_mask     = nullptr; // F32 [n_kv, n_batch]
     ggml_tensor * self_kq_mask_cnv = nullptr; //     [n_kv, n_batch]
 
@@ -343,7 +357,7 @@ public:
     llm_graph_input_one() {}
     virtual ~llm_graph_input_one() = default;
 
-    void set_input(const llama_ubatch *) override;
+    void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * one = nullptr; // F32
 };
diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index ea90e8797..c5e19c77a 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -808,7 +808,7 @@ ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint
             0);
 }
 
-ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const {
+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;
@@ -818,8 +818,8 @@ ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_
 
     k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
 
-    if (kv_idxs && supports_set_rows) {
-        return ggml_set_rows(ctx, k, k_cur, kv_idxs);
+    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
@@ -832,7 +832,7 @@ ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_
     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 * kv_idxs, int32_t il, const slot_info & sinfo) const {
+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;
@@ -842,9 +842,9 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
 
     v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
 
-    if (kv_idxs && supports_set_rows) {
+    if (v_idxs && supports_set_rows) {
         if (!v_trans) {
-            return ggml_set_rows(ctx, v, v_cur, kv_idxs);
+            return ggml_set_rows(ctx, v, v_cur, v_idxs);
         }
 
         // the row becomes a single element
@@ -859,10 +859,10 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
         //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]
-        // kv_idxs [n_tokens, 1,        1]
-        return ggml_set_rows(ctx, v_view, v_cur, kv_idxs);
+        // 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
@@ -885,7 +885,42 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
     return ggml_cpy(ctx, v_cur, v_view);
 }
 
-void llama_kv_cache_unified::set_input_kv_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
+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[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;
     }
@@ -1906,20 +1941,32 @@ ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t
     return kv->get_v(ctx, il, n_kv);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il) const {
-    return kv->cpy_k(ctx, k_cur, kv_idxs, il, sinfos[i_cur]);
+ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
+    return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il) const {
-    return kv->cpy_v(ctx, v_cur, kv_idxs, il, sinfos[i_cur]);
+ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const {
+    return kv->cpy_v(ctx, v_cur, v_idxs, il, sinfos[i_cur]);
+}
+
+ggml_tensor * llama_kv_cache_unified_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    return kv->build_input_k_idxs(ctx, ubatch);
+}
+
+ggml_tensor * llama_kv_cache_unified_context::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    return kv->build_input_v_idxs(ctx, ubatch);
 }
 
 void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
     kv->set_input_k_shift(dst);
 }
 
-void llama_kv_cache_unified_context::set_input_kv_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
-    kv->set_input_kv_idxs(dst, ubatch, sinfos[i_cur]);
+void llama_kv_cache_unified_context::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+    kv->set_input_k_idxs(dst, ubatch, sinfos[i_cur]);
+}
+
+void llama_kv_cache_unified_context::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+    kv->set_input_v_idxs(dst, ubatch, sinfos[i_cur]);
 }
 
 void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
diff --git a/src/llama-kv-cache-unified.h b/src/llama-kv-cache-unified.h
index 1f72afd21..3163b2779 100644
--- a/src/llama-kv-cache-unified.h
+++ b/src/llama-kv-cache-unified.h
@@ -124,8 +124,8 @@ public:
     ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
 
     // store k_cur and v_cur in the cache based on the provided head location
-    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const;
-    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const;
+    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const;
+    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const;
 
     //
     // preparation API
@@ -146,10 +146,15 @@ public:
     void apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch);
 
     //
-    // set_input API
+    // input API
     //
 
-    void set_input_kv_idxs   (ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
+    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+    ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+
+    void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
+    void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
+
     void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
     void set_input_k_shift   (ggml_tensor * dst) const;
     void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
@@ -286,12 +291,16 @@ public:
     ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
 
     // store k_cur and v_cur in the cache based on the provided head location
-    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il) const;
-    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * kv_idxs, int32_t il) const;
+    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;
+    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const;
 
-    void set_input_k_shift(ggml_tensor * dst) const;
+    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+    ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
 
-    void set_input_kv_idxs   (ggml_tensor * dst, const llama_ubatch * ubatch) const;
+    void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;
+    void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;
+
+    void set_input_k_shift   (ggml_tensor * dst) const;
     void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
     void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;