vshmidt/llama.cpp: LLM inference in C/C++

src/models/jais2.cpp
106 строк3 KB
Sigbjørn Skjæret
model : refactor bias tensor variable names (#22079)
18 апр 2026, 21:12
18 апр 2026, 21:124f02d47
#include "models.h"

// JAIS-2 model graph builder
// Uses: LayerNorm (not RMSNorm), relu2 activation, separate Q/K/V, RoPE embeddings
llm_build_jais2::llm_build_jais2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
    const int64_t n_embd_head = hparams.n_embd_head_v();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
    GGML_ASSERT(n_embd_head == n_rot);

    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // inp_pos - contains the positions
    ggml_tensor * inp_pos = build_inp_pos();

    // KV input for attention
    auto * inp_attn = build_attn_inp_kv();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    for (int il = 0; il < n_layer; ++il) {
        // Pre-attention LayerNorm
        cur = build_norm(inpL,
                model.layers[il].attn_norm,
                model.layers[il].attn_norm_b,
                LLM_NORM, il);
        cb(cur, "attn_norm", il);

        // Self-attention with separate Q, K, V projections
        {
            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
                    n_embd_head, n_head, n_head_kv, il);

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

            cb(Qcur, "Qcur_rope", il);
            cb(Kcur, "Kcur_rope", il);

            cur = build_attn(inp_attn,
                    model.layers[il].wo, model.layers[il].wo_b, 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) {
            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
        }

        // Residual connection
        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
        cb(ffn_inp, "ffn_inp", il);

        // Pre-FFN LayerNorm
        cur = build_norm(ffn_inp,
                model.layers[il].ffn_norm,
                model.layers[il].ffn_norm_b,
                LLM_NORM, il);
        cb(cur, "ffn_norm", il);

        // FFN with relu2 activation (ReLU squared) - no gate projection
        // up -> relu2 -> down
        cur = build_ffn(cur,
                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                NULL, NULL, NULL,  // no gate
                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                NULL,
                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
        cb(cur, "ffn_out", il);

        // Residual connection
        inpL = ggml_add(ctx0, cur, ffn_inp);
        inpL = build_cvec(inpL, il);
        cb(inpL, "l_out", il);
    }

    // Final LayerNorm
    cur = build_norm(inpL,
            model.output_norm,
            model.output_norm_b,
            LLM_NORM, -1);
    cb(cur, "result_norm", -1);

    res->t_embd = cur;

    // Output projection
    cur = build_lora_mm(model.output, cur);
    cb(cur, "result_output", -1);

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);
}