llama.cpp/src/models/llm_build_olmo2.cpp

#include "llm_build_olmo2.h"

#include <cmath>

template <bool iswa>
llm_build_olmo2<iswa>::llm_build_olmo2(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 == hparams.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();

        using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
        inp_attn_type * inp_attn = nullptr;

        if constexpr (iswa) {
            inp_attn = build_attn_inp_kv_iswa();
        } else {
            inp_attn = build_attn_inp_kv();
        }
;
        ggml_tensor * inp_out_ids = build_inp_out_ids();

        for (int il = 0; il < n_layer; ++il) {
            ggml_tensor * inpSA = inpL;

            cur = inpL;

            // self_attention
            {
                // compute Q and K and RoPE them
                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
                        LLM_NORM_RMS, il);
                cb(Qcur, "Qcur_normed", il);

                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
                        LLM_NORM_RMS, il);
                cb(Kcur, "Kcur_normed", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

                const bool is_swa = hparams.is_swa(il);

                if (is_swa) {
                    // For sliding window layers, Olmo3 use regular rope with no yarn rope scaling.
                    // This is achieved here by setting freq_scale and attn_factor to 1.
                    // We also set ext_factor to 0 to avoid a few unnecessary computations.
                    Qcur = ggml_rope_ext(
                        ctx0, Qcur, inp_pos, nullptr,
                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
                        0.0, 1.0, beta_fast, beta_slow
                        );

                    Kcur = ggml_rope_ext(
                        ctx0, Kcur, inp_pos, nullptr,
                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
                        0.0, 1.0, beta_fast, beta_slow
                        );
                } else {
                    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", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                cur = build_attn(inp_attn,
                        model.layers[il].wo, NULL,
                        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);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }
;
            cur = build_norm(cur,
                    model.layers[il].attn_post_norm, NULL,
                    LLM_NORM_RMS, il);
            cb(cur, "attn_post_norm", il);

            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = build_ffn(ffn_inp,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, il);
            cb(cur, "ffn_out", il);

            cur = build_norm(cur,
                    model.layers[il].ffn_post_norm, NULL,
                    LLM_NORM_RMS, -1);
            cb(cur, "ffn_post_norm", -1);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = build_cvec(cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }
;
        cur = inpL;

        cur = build_norm(cur,
                model.output_norm, NULL,
                LLM_NORM_RMS, -1);

        cb(cur, "result_norm", -1);
        res->t_embd = cur;

        // lm_head
        cur = build_lora_mm(model.output, cur);

        cb(cur, "result_output", -1);
        res->t_logits = cur;

        ggml_build_forward_expand(gf, cur);
    }

// Explicit template instantiations
template struct llm_build_olmo2<false>;
template struct llm_build_olmo2<true>;