1#include "models.h"
  2
  3llm_build_neo_bert::llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
  4    const int64_t n_embd_head = hparams.n_embd_head_v;
  5    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
  6
  7    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
  8
  9    ggml_tensor * cur;
 10    ggml_tensor * inpL;
 11    ggml_tensor * inp_pos = build_inp_pos();
 12
 13    // construct input embeddings (token, type, position)
 14    inpL = build_inp_embd(model.tok_embd);
 15    cb(inpL, "inp_embd", -1);
 16
 17    auto * inp_attn = build_attn_inp_no_cache();
 18
 19    ggml_tensor * inp_out_ids = build_inp_out_ids();
 20
 21    for (int il = 0; il < n_layer; ++il) {
 22        ggml_tensor * cur = inpL;
 23
 24        // pre-norm
 25        cur = build_norm(inpL,
 26                model.layers[il].attn_norm, NULL,
 27                LLM_NORM_RMS, il);
 28
 29        {
 30            ggml_tensor * Qcur;
 31            ggml_tensor * Kcur;
 32            ggml_tensor * Vcur;
 33
 34            // self-attention
 35            cur = build_lora_mm(model.layers[il].wqkv, cur);
 36            cb(cur, "wqkv", il);
 37
 38            Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
 39            Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd));
 40            Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa));
 41
 42            // RoPE
 43            Qcur = ggml_rope_ext(
 44                    ctx0, Qcur, inp_pos, nullptr,
 45                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 46                    ext_factor, attn_factor, beta_fast, beta_slow
 47                    );
 48
 49            Kcur = ggml_rope_ext(
 50                    ctx0, Kcur, inp_pos, nullptr,
 51                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 52                    ext_factor, attn_factor, beta_fast, beta_slow
 53                    );
 54
 55            cb(Qcur, "Qcur", il);
 56            cb(Kcur, "Kcur", il);
 57            cb(Vcur, "Vcur", il);
 58
 59            cur = build_attn(inp_attn,
 60                    model.layers[il].wo, nullptr,
 61                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 62            cb(cur, "kqv_out", il);
 63        }
 64        if (il == n_layer - 1 && inp_out_ids) {
 65            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
 66            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
 67        }
 68        // re-add the layer input
 69        cur = ggml_add(ctx0, cur, inpL);
 70
 71        ggml_tensor * ffn_inp = cur;
 72        cb(ffn_inp, "ffn_inp", il);
 73
 74        // pre-norm
 75        cur = build_norm(ffn_inp,
 76                model.layers[il].ffn_norm, NULL,
 77                LLM_NORM_RMS, il);
 78        cb(cur, "ffn_norm", il);
 79
 80        // feed-forward network
 81        cur = build_ffn(cur,
 82                model.layers[il].ffn_up,
 83                NULL, NULL, NULL, NULL, NULL,
 84                model.layers[il].ffn_down,
 85                NULL, NULL, NULL,
 86                LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
 87
 88        // attentions bypass the intermediate layer
 89        cur = ggml_add(ctx0, cur, ffn_inp);
 90
 91        // input for next layer
 92        inpL = cur;
 93    }
 94    cur = inpL;
 95
 96    cur = build_norm(cur,
 97            model.output_norm_enc, NULL,
 98            LLM_NORM_RMS, -1);
 99
100    cb(cur, "result_embd", -1);
101    res->t_embd = cur;
102
103    ggml_build_forward_expand(gf, cur);
104}