models : consolidate nextn row cropping into shared helpers (#30017)

* mimo2 : always emit h_nextn

the other nextn-capable models set it unconditionally

Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD

* models : consolidate nextn row cropping into shared helpers

- replace the duplicated crop conditions and the per-model flags (narrow_early,
  crop_before_ffn, crop_last_layer, emit_h_nextn) with two helpers on llm_graph_context:
  crop_before_nextn() / crop_after_nextn()
- models that only tested embeddings_nextn_masked now share the same condition, so they
  crop the last layer before the nextn capture whenever extraction is off
- t_h_nextn is now set unconditionally in mimo2, qwen4exp and deepseek4 (as in the other
  nextn-capable models); host-side reads stay gated by cparams.embeddings_nextn

Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-MOPD

src/llama-graph.h

@@ -1053,6 +1053,16 @@ struct llm_graph_context {
 
     void cb(ggml_tensor * cur, const char * name, int il) const;
 
+    // true when the last layer must be narrowed to the output rows before the nextn hidden state is captured
+    bool crop_before_nextn(const ggml_tensor * inp_out_ids) const {
+        return inp_out_ids != nullptr && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked);
+    }
+
+    // true when the nextn hidden state must be narrowed to the output rows after it is captured
+    bool crop_after_nextn(const ggml_tensor * inp_out_ids) const {
+        return inp_out_ids != nullptr && cparams.embeddings_nextn && !cparams.embeddings_nextn_masked;
+    }
+
     //
     // common
     //

src/models/bailingmoe3.cpp

@@ -363,7 +363,7 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
             cb(cur, "mla_out", il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -408,7 +408,7 @@ llama_model_bailingmoe3::graph::graph(const llama_model & model, const llm_graph
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/cohere2moe.cpp

@@ -205,7 +205,7 @@ llama_model_cohere2moe::graph::graph(const llama_model & model, const llm_graph_
                     1.0f / sqrtf(float(n_embd_head)), il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpL    = ggml_get_rows(ctx0, inpL, inp_out_ids);
             ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
@@ -269,7 +269,7 @@ llama_model_cohere2moe::graph::graph(const llama_model & model, const llm_graph_
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/deepseek2.cpp

@@ -634,7 +634,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
                             Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
             }
         }
-        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -697,7 +697,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/deepseek32.cpp

@@ -402,7 +402,7 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
         }
         // when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,
         // so the early output masking has to be skipped (it is applied after the final norm instead)
-        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -469,7 +469,7 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/deepseek4.cpp

@@ -1335,11 +1335,9 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
     ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens);
     ggml_tensor * flat_out = inp_out_ids ? ggml_get_rows(ctx0, flat, inp_out_ids) : flat;
 
-    if (cparams.embeddings_nextn) {
-        ggml_tensor * h_nextn = cparams.embeddings_nextn_masked ? flat_out : inpL;
-        cb(h_nextn, "h_nextn", -1);
-        res->t_h_nextn = h_nextn;
-    }
+    ggml_tensor * h_nextn = cparams.embeddings_nextn_masked ? flat_out : inpL;
+    cb(h_nextn, "h_nextn", -1);
+    res->t_h_nextn = h_nextn;
 
     if (inp_out_ids) {
         inpL = ggml_reshape_3d(ctx0, flat_out, n_embd, hc, n_outputs);

src/models/gemma4.cpp

@@ -274,7 +274,7 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
 
         // TODO @ngxson : strip unused token right after the last KV layer to speed up prompt processing
         // keep all rows when extracting unmasked nextn embeddings (MTP target needs the hidden state for every token)
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
             inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
         }
@@ -374,7 +374,7 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
             ggml_tensor * inp_this_layer = gemma4_view_2d_slice(ctx0, inp_per_layer, il); // [n_embd_per_layer, n_tokens]
 
             // TODO @ngxson : improve this
-            if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+            if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
                 inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);
             }
 
@@ -412,7 +412,7 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/glm-dsa.cpp

@@ -446,7 +446,7 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
         }
         // when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,
         // so the early output masking has to be skipped (it is applied after the final norm instead)
-        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -513,7 +513,7 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/glm4-moe.cpp

@@ -365,7 +365,7 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
                     model.layers[il].wo, NULL, model.layers[il].wo_s,
                     Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
         }
-        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -428,7 +428,7 @@ llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_pa
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/glm5-next.cpp

@@ -650,8 +650,7 @@ llama_model_glm5_next::graph::graph(const llama_model & model, const llm_graph_p
 
     // narrow to the output tokens, then collapse the streams
     // Unmasked nextn embeddings need all rows.
-    const bool narrow_early = inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked);
-    if (narrow_early) {
+    if (crop_before_nextn(inp_out_ids)) {
         ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens);
         flat = ggml_get_rows(ctx0, flat, inp_out_ids);
         inpL = ggml_reshape_3d(ctx0, flat, n_embd, hc, n_outputs);
@@ -666,7 +665,7 @@ llama_model_glm5_next::graph::graph(const llama_model & model, const llm_graph_p
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (inp_out_ids && !narrow_early) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
     cb(cur, "result_norm", -1);

src/models/hy-v3.cpp

@@ -148,7 +148,7 @@ llama_model_hy_v3::graph::graph(const llama_model & model, const llm_graph_param
             cb(cur, "attn_out", il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -216,7 +216,7 @@ llama_model_hy_v3::graph::graph(const llama_model & model, const llm_graph_param
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/mimo2.cpp

@@ -101,9 +101,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     const float v_scale = hparams.f_attn_value_scale;
-    const bool emit_h_nextn = cparams.embeddings_nextn;
     const bool extract_final_inp = (size_t) n_layer < cparams.embeddings_layer_inp.size() && cparams.embeddings_layer_inp[n_layer];
-    const bool crop_last_layer = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked) && !extract_final_inp;
 
     for (int il = 0; il < n_layer; ++il) {
         res->t_layer_inp[il] = inpL;
@@ -185,7 +183,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
             }
         }
 
-        if (il == n_layer - 1 && crop_last_layer) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids) && !extract_final_inp) {
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -236,18 +234,17 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param
     cur = inpL;
     if (extract_final_inp) {
         res->t_layer_inp[n_layer] = cur;
-        if (inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked)) {
+        if (crop_before_nextn(inp_out_ids)) {
             cur = ggml_get_rows(ctx0, cur, inp_out_ids);
         }
     }
 
-    if (emit_h_nextn) {
-        cb(cur, "h_nextn", -1);
-        res->t_h_nextn = cur;
+    // pre-norm state for MTP; set even when extraction is off
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
 
-        if (!cparams.embeddings_nextn_masked && inp_out_ids) {
-            cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-        }
+    if (crop_after_nextn(inp_out_ids)) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 
     cur = build_norm(cur,

src/models/nemotron-h-moe.cpp

@@ -52,11 +52,6 @@ llama_model_nemotron_h_moe::graph_mtp::graph_mtp(const llama_model & model, cons
 
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
-    // attention fills KV over all tokens, but the MoE is position-wise: gather output rows before
-    // it to save FFN compute (unless unmasked embeddings_nextn needs the full-length hidden state)
-    const bool emit_h_nextn    = cparams.embeddings_nextn;
-    const bool crop_before_ffn = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked);
-
     auto * inp_attn = build_attn_inp_kv();
 
     ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
@@ -88,7 +83,7 @@ llama_model_nemotron_h_moe::graph_mtp::graph_mtp(const llama_model & model, cons
     cb(cur, "mtp_attn_residual", il);
 
     // gather the output rows here so the MoE FFN below only runs on the positions we keep
-    if (crop_before_ffn) {
+    if (crop_before_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 
@@ -149,7 +144,7 @@ llama_model_nemotron_h_moe::graph_mtp::graph_mtp(const llama_model & model, cons
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!crop_before_ffn && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/nemotron-h.cpp

@@ -220,7 +220,7 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
             cur = build_ffn_layer(cur, model, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked && !extract_final_inp) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids) && !extract_final_inp) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -237,7 +237,7 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
     if (extract_final_inp) {
         res->t_layer_inp[n_layer] = cur;
 
-        if (inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (crop_before_nextn(inp_out_ids)) {
             cur = ggml_get_rows(ctx0, cur, inp_out_ids);
         }
     }
@@ -248,7 +248,7 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/openai-moe.cpp

@@ -114,7 +114,7 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
 
             cb(cur, "attn_out", il);
         }
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             // skip computing output for unused tokens
             cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
@@ -154,7 +154,7 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
 
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/qwen35.cpp

@@ -175,7 +175,7 @@ llama_model_qwen35::graph::graph(const llama_model & model, const llm_graph_para
             cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur,   inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -212,7 +212,7 @@ llama_model_qwen35::graph::graph(const llama_model & model, const llm_graph_para
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/qwen35moe.cpp

@@ -194,7 +194,7 @@ llama_model_qwen35moe::graph::graph(const llama_model & model, const llm_graph_p
             cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -232,7 +232,7 @@ llama_model_qwen35moe::graph::graph(const llama_model & model, const llm_graph_p
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/qwen3next.cpp

@@ -168,7 +168,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
             cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -206,7 +206,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }
 

src/models/qwen4exp.cpp

@@ -470,7 +470,7 @@ llama_model_qwen4exp::graph::graph(const llama_model & model, const llm_graph_pa
             cur = build_layer_attn(inp->get_attn(), mctx_hyb, inp_kpool, cur, inp_pos, sections, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             // everything below is per token, so drop the rows that produce no output
             cur    = ggml_get_rows(ctx0, cur,    inp_out_ids);
             inject = ggml_get_rows(ctx0, inject, inp_out_ids);
@@ -499,13 +499,11 @@ llama_model_qwen4exp::graph::graph(const llama_model & model, const llm_graph_pa
     }
 
     // the MTP head reads the hc-wide residual, before the final mixer
-    if (cparams.embeddings_nextn) {
-        res->t_h_nextn = ggml_reshape_2d(ctx0, res_hc, n_embd*hc, res_hc->ne[2]);
-        cb(res->t_h_nextn, "h_nextn", -1);
-        ggml_build_forward_expand(gf, res->t_h_nextn);
-    }
+    res->t_h_nextn = ggml_reshape_2d(ctx0, res_hc, n_embd*hc, res_hc->ne[2]);
+    cb(res->t_h_nextn, "h_nextn", -1);
+    ggml_build_forward_expand(gf, res->t_h_nextn);
 
-    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         res_hc = ggml_reshape_2d(ctx0, res_hc, n_embd*hc, res_hc->ne[2]);
         res_hc = ggml_get_rows(ctx0, res_hc, inp_out_ids);
         res_hc = ggml_reshape_3d(ctx0, res_hc, n_embd, hc, res_hc->ne[1]);

src/models/step35.cpp

@@ -287,7 +287,7 @@ llama_model_step35::graph::graph(const llama_model & model, const llm_graph_para
             cb(cur, "attn_proj", il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && crop_before_nextn(inp_out_ids)) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -349,7 +349,7 @@ llama_model_step35::graph::graph(const llama_model & model, const llm_graph_para
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;
 
-    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+    if (crop_after_nextn(inp_out_ids)) {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
     }