model: use ggml_rope_set_offset() (#27382)

* model: use ggml_rope_set_offset()

* partially apply to deepseek2

docs/development/HOWTO-add-model.md

@@ -166,6 +166,19 @@ Examples:
 - Some models require scaling the input position. For example, `[0, 1, 2, ...]` becomes `[0, 0.5, 1, ...]`. In this case, you can provide the scaling via `freq_scale = 0.5f`.
 - Some models use learned RoPE frequencies instead of relying on `powf(freq_base, -2.0 * i / n_dims)`. In this case, you can provide the learned frequencies via the `rope_freqs` tensor (corresponding to the `c` argument in `ggml_rope_ext`), then set `freq_base = 1.0f`. An important note is that `rope_freqs` in GGML is the **inverse** (`theta = pos[i] / rope_freqs`), so you may need to invert `rope_freqs` during conversion.
 
+### Rotating only a part of the head
+
+Many models rotate only a part of each head and leave the rest untouched (often called the "nope" part). Do not build this with views plus `ggml_concat`, it's not efficient. Both layouts can be done with a single RoPE op:
+
+- `[rope|nope]`, rotated dims first: pass `n_dims` smaller than the head size to `ggml_rope_ext`. Dims from `n_dims` to the end are copied as-is.
+- `[nope|rope]`, rotated dims last: call `ggml_rope_set_offset(cur, n_offs)` on the result of the RoPE, where `n_offs` is the size of the leading untouched part. Dims outside `[n_offs, n_offs + n_dims)` are copied as-is.
+
+`n_offs` must be even, `n_offs + n_dims` must fit in the row, and vision RoPE is not supported. Note that the frequencies are computed relative to the rotated window.
+
+Example: DeepSeek-V4 uses `[nope|rope]` for its query, key and compressed KV tensors, so `src/models/deepseek4.cpp` ropes the whole tensor and then calls `ggml_rope_set_offset(cur, n_embd_head_nope)`.
+
+Exception: some models apply an extra op to the `nope` part, for example `deepseek32.cpp`, and may not use this optimization. While RoPE can be applied selectively to a part of the head, the extra op may not, so these models still need views plus `ggml_concat`.
+
 ## GGUF specification
 
 https://github.com/ggml-org/ggml/blob/master/docs/gguf.md

src/models/deepseek2.cpp

@@ -524,17 +524,9 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
                 q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                 cb(q, "q", il);
             }
-            // split into {n_embd_head_qk_nope, n_head, n_tokens}
-            ggml_tensor * q_nope =
-                ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
-                             ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
-            cb(q_nope, "q_nope", il);
-
-            // and {n_embd_head_qk_rope, n_head, n_tokens}
-            ggml_tensor * q_pe = ggml_view_3d(
-                ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
-                ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
-            cb(q_pe, "q_pe", il);
+            // {n_embd_head_k, n_head, n_tokens}
+            q = ggml_reshape_3d(ctx0, q, n_embd_head_k, n_head, n_tokens);
+            cb(q, "q", il);
 
             ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
             cb(kv_cmpr_pe, "kv_cmpr_pe", il);
@@ -552,10 +544,6 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
                                               ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
             cb(k_pe, "k_pe", il);
 
-            q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                                 ext_factor, attn_factor, beta_fast, beta_slow);
-            cb(q_pe, "q_pe", il);
-
             k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                                  ext_factor, attn_factor, beta_fast, beta_slow);
             cb(k_pe, "k_pe", il);
@@ -564,6 +552,20 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
             cb(kv_cmpr, "kv_cmpr", il);
 
             if (is_mla) {
+                // split into {n_embd_head_qk_nope, n_head, n_tokens}
+                ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+                                                    q->nb[1], q->nb[2], 0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_embd_head_qk_rope, n_head, n_tokens}
+                ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+                                                  q->nb[1], q->nb[2], ggml_row_size(q->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                                     ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(q_pe, "q_pe", il);
+
                 // {n_embd_head_qk_nope, n_tokens, n_head}
                 q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
                 cb(q_nope, "q_nope_perm", il);
@@ -623,10 +625,14 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
                 Vcur = ggml_cont(ctx0, Vcur);
                 cb(Vcur, "Vcur_cont", il);
 
-                ggml_tensor * Qcur = ggml_concat(ctx0, q_nope, q_pe, 0);
+                // RoPE is applied to the trailing dims only
+                ggml_tensor * Qcur = ggml_rope_ext(ctx0, q, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+                                                   freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+                Qcur = ggml_rope_set_offset(Qcur, n_embd_head_qk_nope);
                 cb(Qcur, "Qcur", il);
 
-                ggml_tensor * Kcur = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+                ggml_tensor * Kcur = ggml_concat(ctx0, k_nope,
+                        ggml_repeat_4d(ctx0, k_pe, n_embd_head_qk_rope, n_head, n_tokens, 1), 0);
                 cb(Kcur, "Kcur", il);
 
                 if (inp_attn_scale) {

src/models/deepseek4.cpp

@@ -501,21 +501,10 @@ ggml_tensor * llama_model_deepseek4::graph::build_hca_compressed_kv_from_state(
     comp = build_norm(comp, norm, nullptr, LLM_NORM_RMS, il);
     cb(comp, name, il);
 
-    ggml_tensor * comp_nope = ggml_view_3d(ctx0, comp, n_embd_head_nope, 1, n_blocks,
-            ggml_row_size(comp->type, n_embd_head),
-            ggml_row_size(comp->type, n_embd_head),
-            0);
-    ggml_tensor * comp_pe = ggml_view_3d(ctx0, comp, n_embd_head_rope, 1, n_blocks,
-            ggml_row_size(comp->type, n_embd_head),
-            ggml_row_size(comp->type, n_embd_head),
-            ggml_row_size(comp->type, n_embd_head_nope));
-
-    comp_pe = ggml_rope_ext(ctx0, comp_pe, comp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig,
+    comp = ggml_rope_ext(ctx0, comp, comp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig,
             hparams.dsv4_compress_rope_base, freq_scale, ext_factor,
             dsv4_rope_attn_factor(freq_scale, ext_factor), beta_fast, beta_slow);
-    cb(comp_pe, name, il);
-
-    comp = ggml_concat(ctx0, comp_nope, comp_pe, 0);
+    comp = ggml_rope_set_offset(comp, n_embd_head_nope);
     cb(comp, name, il);
 
     return comp;
@@ -585,21 +574,10 @@ ggml_tensor * llama_model_deepseek4::graph::build_overlap_compressed_kv_from_sta
     comp = build_norm(comp, norm, nullptr, LLM_NORM_RMS, il);
     cb(comp, name, il);
 
-    ggml_tensor * comp_nope = ggml_view_3d(ctx0, comp, n_embd_head_nope, 1, n_blocks,
-            ggml_row_size(comp->type, n_embd_head),
-            ggml_row_size(comp->type, n_embd_head),
-            0);
-    ggml_tensor * comp_pe = ggml_view_3d(ctx0, comp, n_embd_head_rope, 1, n_blocks,
-            ggml_row_size(comp->type, n_embd_head),
-            ggml_row_size(comp->type, n_embd_head),
-            ggml_row_size(comp->type, n_embd_head_nope));
-
-    comp_pe = ggml_rope_ext(ctx0, comp_pe, comp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig,
+    comp = ggml_rope_ext(ctx0, comp, comp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig,
             hparams.dsv4_compress_rope_base, freq_scale, ext_factor,
             dsv4_rope_attn_factor(freq_scale, ext_factor), beta_fast, beta_slow);
-    cb(comp_pe, name, il);
-
-    comp = ggml_concat(ctx0, comp_nope, comp_pe, 0);
+    comp = ggml_rope_set_offset(comp, n_embd_head_nope);
     cb(comp, name, il);
 
     return comp;
@@ -628,21 +606,12 @@ ggml_tensor * llama_model_deepseek4::graph::build_lid_top_k(
     indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, nt);
     cb(indexer_q, "lid_q", il);
 
-    ggml_tensor * indexer_q_nope = ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, nt,
-            ggml_row_size(indexer_q->type, n_embd_indexer_head),
-            ggml_row_size(indexer_q->type, n_embd_indexer_head)*n_indexer_head,
-            0);
-    ggml_tensor * indexer_q_pe = ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, nt,
-            ggml_row_size(indexer_q->type, n_embd_indexer_head),
-            ggml_row_size(indexer_q->type, n_embd_indexer_head)*n_indexer_head,
-            ggml_row_size(indexer_q->type, n_embd_indexer_head_nope));
-
-    indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_embd_indexer_head_rope,
+    indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_embd_indexer_head_rope,
             rope_type, n_ctx_orig, hparams.dsv4_compress_rope_base, freq_scale,
             ext_factor, dsv4_rope_attn_factor(freq_scale, ext_factor), beta_fast, beta_slow);
-    cb(indexer_q_pe, "lid_q_pe", il);
+    indexer_q = ggml_rope_set_offset(indexer_q, n_embd_indexer_head_nope);
+    cb(indexer_q, "lid_q_rope", il);
 
-    indexer_q = ggml_concat(ctx0, indexer_q_nope, indexer_q_pe, 0);
     indexer_q = llama_mul_mat_hadamard(ctx0, indexer_q, inp_lid.k_rot);
     cb(indexer_q, "lid_q_rot", il);
 
@@ -945,18 +914,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention_impl(
     q = ggml_rms_norm(ctx0, q, norm_rms_eps);
     cb(q, "q_norm", il);
 
-    ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_nope, n_head, nt,
-            ggml_row_size(q->type, n_embd_head),
-            ggml_row_size(q->type, n_embd_head)*n_head,
-            0);
-    ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_rope, n_head, nt,
-            ggml_row_size(q->type, n_embd_head),
-            ggml_row_size(q->type, n_embd_head)*n_head,
-            ggml_row_size(q->type, n_embd_head_nope));
-    q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
+    q = ggml_rope_ext(ctx0, q, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
             freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
-    cb(q_pe, "q_pe", il);
-    q = ggml_concat(ctx0, q_nope, q_pe, 0);
+    q = ggml_rope_set_offset(q, n_embd_head_nope);
     cb(q, "q", il);
 
     ggml_tensor * kv = build_lora_mm(layer.wkv, cur);
@@ -964,18 +924,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention_impl(
     kv = ggml_reshape_3d(ctx0, kv, n_embd_head, 1, nt);
     cb(kv, "kv_norm", il);
 
-    ggml_tensor * kv_nope = ggml_view_3d(ctx0, kv, n_embd_head_nope, 1, nt,
-            ggml_row_size(kv->type, n_embd_head),
-            ggml_row_size(kv->type, n_embd_head),
-            0);
-    ggml_tensor * kv_pe = ggml_view_3d(ctx0, kv, n_embd_head_rope, 1, nt,
-            ggml_row_size(kv->type, n_embd_head),
-            ggml_row_size(kv->type, n_embd_head),
-            ggml_row_size(kv->type, n_embd_head_nope));
-    kv_pe = ggml_rope_ext(ctx0, kv_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
+    kv = ggml_rope_ext(ctx0, kv, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
             freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
-    cb(kv_pe, "kv_pe", il);
-    kv = ggml_concat(ctx0, kv_nope, kv_pe, 0);
+    kv = ggml_rope_set_offset(kv, n_embd_head_nope);
     cb(kv, "kv", il);
 
     const int64_t ratio = hparams.dsv4_compress_ratios[il];
@@ -1245,17 +1196,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention_impl(
     }
 
     out = ggml_reshape_3d(ctx0, out, n_embd_head, n_head, nt);
-    ggml_tensor * out_nope = ggml_view_3d(ctx0, out, n_embd_head_nope, n_head, nt,
-            ggml_row_size(out->type, n_embd_head),
-            ggml_row_size(out->type, n_embd_head)*n_head,
-            0);
-    ggml_tensor * out_pe = ggml_view_3d(ctx0, out, n_embd_head_rope, n_head, nt,
-            ggml_row_size(out->type, n_embd_head),
-            ggml_row_size(out->type, n_embd_head)*n_head,
-            ggml_row_size(out->type, n_embd_head_nope));
-    out_pe = ggml_rope_ext_back(ctx0, out_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
+    out = ggml_rope_ext_back(ctx0, out, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
             freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
-    out = ggml_concat(ctx0, out_nope, out_pe, 0);
+    out = ggml_rope_set_offset(out, n_embd_head_nope);
     cb(out, "attn_derope", il);
 
     out = ggml_reshape_3d(ctx0, out, o_group_dim, n_groups, nt);

src/models/dflash.cpp

@@ -591,17 +591,9 @@ llama_model_dflash::graph_dsv4::graph_dsv4(const llama_model & model, const llm_
             kv = build_norm(kv, layer.attn_kv_norm, nullptr, LLM_NORM_RMS, il);
             kv = ggml_reshape_3d(ctx0, kv, n_embd_head, 1, n_tokens);
 
-            ggml_tensor * kv_nope = ggml_view_3d(ctx0, kv, n_embd_head_nope, 1, n_tokens,
-                    ggml_row_size(kv->type, n_embd_head),
-                    ggml_row_size(kv->type, n_embd_head),
-                    0);
-            ggml_tensor * kv_pe = ggml_view_3d(ctx0, kv, n_embd_head_rope, 1, n_tokens,
-                    ggml_row_size(kv->type, n_embd_head),
-                    ggml_row_size(kv->type, n_embd_head),
-                    ggml_row_size(kv->type, n_embd_head_nope));
-            kv_pe = ggml_rope_ext(ctx0, kv_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, 0,
+            kv = ggml_rope_ext(ctx0, kv, inp_pos, nullptr, n_embd_head_rope, rope_type, 0,
                     freq_base, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
-            kv = ggml_concat(ctx0, kv_nope, kv_pe, 0);
+            kv = ggml_rope_set_offset(kv, n_embd_head_nope);
             cb(kv, "kv_injected", il);
 
             if (inp_attn->self_k_rot_swa) {

src/models/minicpm3.cpp

@@ -115,19 +115,9 @@ llama_model_minicpm3::graph::graph(const llama_model & model, const llm_graph_pa
             q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
             cb(q, "q", il);
 
-            // split into {n_head * n_embd_head_qk_nope, n_tokens}
-            ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
-                    ggml_row_size(q->type, hparams.n_embd_head_k()),
-                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),
-                    0);
-            cb(q_nope, "q_nope", il);
-
-            // and {n_head * n_embd_head_qk_rope, n_tokens}
-            ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
-                    ggml_row_size(q->type, hparams.n_embd_head_k()),
-                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),
-                    ggml_row_size(q->type, n_embd_head_qk_nope));
-            cb(q_pe, "q_pe", il);
+            // {n_embd_head_k, n_head, n_tokens}, RoPE is applied to the trailing dims only
+            q = ggml_reshape_3d(ctx0, q, hparams.n_embd_head_k(), n_head, n_tokens);
+            cb(q, "q", il);
 
             // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
             ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
@@ -172,12 +162,13 @@ llama_model_minicpm3::graph::graph(const llama_model & model, const llm_graph_pa
             v_states = ggml_cont(ctx0, v_states);
             cb(v_states, "v_states", il);
 
-            q_pe = ggml_rope_ext(
-                    ctx0, q_pe, inp_pos, rope_factors,
+            q = ggml_rope_ext(
+                    ctx0, q, inp_pos, rope_factors,
                     n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                     );
-            cb(q_pe, "q_pe", il);
+            q = ggml_rope_set_offset(q, n_embd_head_qk_nope);
+            cb(q, "q_rope", il);
 
             // shared RoPE key
             k_pe = ggml_rope_ext(
@@ -187,10 +178,11 @@ llama_model_minicpm3::graph::graph(const llama_model & model, const llm_graph_pa
                     );
             cb(k_pe, "k_pe", il);
 
-            ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+            ggml_tensor * q_states = q;
             cb(q_states, "q_states", il);
 
-            ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+            ggml_tensor * k_states = ggml_concat(ctx0, k_nope,
+                    ggml_repeat_4d(ctx0, k_pe, n_embd_head_qk_rope, n_head, n_tokens, 1), 0);
             cb(k_states, "k_states", il);
 
             cur = build_attn(inp_attn,

src/models/plm.cpp

@@ -81,19 +81,9 @@ llama_model_plm::graph::graph(const llama_model & model, const llm_graph_params
             q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
             cb(q, "q", il);
 
-            // split into {n_head * n_embd_head_qk_nope, n_tokens}
-            ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
-                    ggml_row_size(q->type, hparams.n_embd_head_k()),
-                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),
-                    0);
-            cb(q_nope, "q_nope", il);
-
-            // and {n_head * n_embd_head_qk_rope, n_tokens}
-            ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
-                    ggml_row_size(q->type, hparams.n_embd_head_k()),
-                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),
-                    ggml_row_size(q->type, n_embd_head_qk_nope));
-            cb(q_pe, "q_pe", il);
+            // {n_embd_head_k, n_head, n_tokens}, RoPE is applied to the trailing dims only
+            q = ggml_reshape_3d(ctx0, q, hparams.n_embd_head_k(), n_head, n_tokens);
+            cb(q, "q", il);
 
             // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
             ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
@@ -143,12 +133,13 @@ llama_model_plm::graph::graph(const llama_model & model, const llm_graph_params
                     0);
             cb(v_states, "v_states", il);
 
-            q_pe = ggml_rope_ext(
-                    ctx0, q_pe, inp_pos, nullptr,
+            q = ggml_rope_ext(
+                    ctx0, q, inp_pos, nullptr,
                     n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                     );
-            cb(q_pe, "q_pe", il);
+            q = ggml_rope_set_offset(q, n_embd_head_qk_nope);
+            cb(q, "q_rope", il);
 
             // shared RoPE key
             k_pe = ggml_rope_ext(
@@ -158,10 +149,11 @@ llama_model_plm::graph::graph(const llama_model & model, const llm_graph_params
                     );
             cb(k_pe, "k_pe", il);
 
-            ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+            ggml_tensor * q_states = q;
             cb(q_states, "q_states", il);
 
-            ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+            ggml_tensor * k_states = ggml_concat(ctx0, k_nope,
+                    ggml_repeat_4d(ctx0, k_pe, n_embd_head_qk_rope, n_head, n_tokens, 1), 0);
             cb(k_states, "k_states", il);
 
             cur = build_attn(inp_attn,