Repository: ggerganov/llama.cpp
File: src/models/modern-bert.cpp (C++)

Code before:
            cur = build_norm(inpL,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM, il);
            cb(cur, "attn_norm", il);
        }

        // self attention
        cur = build_lora_mm(model.layers[il].wqkv, cur);
        cb(cur, "wqkv", il);

        const size_t type_size = ggml_type_size(cur->type);

        Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd));
        Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd));
        Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa));

        // RoPE
        Qcur = ggml_rope_ext(
                ctx0, Qcur, inp_pos, nullptr,
                n_rot, rope_type, n_ctx_orig, rope_theta, freq_scale,
                ext_factor, attn_factor, beta_fast, beta_slow
                );

        Kcur = ggml_rope_ext(
                ctx0, Kcur, inp_pos, nullptr,
                n_rot, rope_type, n_ctx_orig, rope_theta, freq_scale,
                ext_factor, attn_factor, beta_fast, beta_slow
                );

        cb(Qcur, "Qcur", il);
        cb(Kcur, "Kcur", il);
        cb(Vcur, "Vcur", il);

        cur = build_attn(inp_attn,
                    model.layers[il].wo, nullptr,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
        cb(cur, "kqv_out", il);

        if (il == n_layer - 1 && inp_out_ids) {
            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
        }

        // re-add the layer input
        cur = ggml_add(ctx0, cur, inpL);

        ggml_tensor * ffn_inp = cur;
        // attention layer norm
        cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM, il);

        cb(ffn_inp, "ffn_inp", il);

Review comment (suggestion):
```suggestion
                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
```

Code after:
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM, il);
            cb(cur, "attn_norm", il);
        }

        // self attention
        cur = build_lora_mm(model.layers[il].wqkv, cur);
        cb(cur, "wqkv", il);

        const size_t type_size = ggml_type_size(cur->type);

        ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd));
        ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd));
        ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa));

        // RoPE
        Qcur = ggml_rope_ext(
                ctx0, Qcur, inp_pos, nullptr,
                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
                ext_factor, attn_factor, beta_fast, beta_slow
                );

        Kcur = ggml_rope_ext(
                ctx0, Kcur, inp_pos, nullptr,
                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
                ext_factor, attn_factor, beta_fast, beta_slow
                );

        cb(Qcur, "Qcur", il);
        cb(Kcur, "Kcur", il);
        cb(Vcur, "Vcur", il);

        cur = build_attn(inp_attn,
                    model.layers[il].wo, nullptr,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
        cb(cur, "kqv_out", il);

        if (il == n_layer - 1 && inp_out_ids) {
            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
        }

        // re-add the layer input
        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
        cb(ffn_inp, "ffn_inp", il);

        // attention layer norm
        cur = build_norm(ffn_inp,
                model.layers[il].ffn_norm, NULL,
                LLM_NORM, il);
        cb(cur, "ffn_norm", il);