1#include "models.h"
  2
  3llm_build_nemotron::llm_build_nemotron(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
  6    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
  7    //GGML_ASSERT(n_embd_head == hparams.n_rot);
  8
  9    ggml_tensor * cur;
 10    ggml_tensor * inpL;
 11
 12    inpL = build_inp_embd(model.tok_embd);
 13
 14    // inp_pos - contains the positions
 15    ggml_tensor * inp_pos = build_inp_pos();
 16
 17    auto * inp_attn = build_attn_inp_kv();
 18
 19    ggml_tensor * inp_out_ids = build_inp_out_ids();
 20
 21    for (int il = 0; il < n_layer; ++il) {
 22        ggml_tensor * inpSA = inpL;
 23
 24        // norm
 25        cur = build_norm(inpL,
 26                model.layers[il].attn_norm,
 27                model.layers[il].attn_norm_b,
 28                LLM_NORM, il);
 29        cb(cur, "attn_norm", il);
 30
 31        // self-attention
 32        {
 33            // compute Q and K and RoPE them
 34            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
 35            cb(Qcur, "Qcur", il);
 36            if (model.layers[il].bq) {
 37                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
 38                cb(Qcur, "Qcur", il);
 39            }
 40            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
 41            cb(Kcur, "Kcur", il);
 42            if (model.layers[il].bk) {
 43                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
 44                cb(Kcur, "Kcur", il);
 45            }
 46            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
 47            cb(Vcur, "Vcur", il);
 48            if (model.layers[il].bv) {
 49                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
 50                cb(Vcur, "Vcur", il);
 51            }
 52            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
 53            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 54            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
 55
 56            Qcur = ggml_rope_ext(
 57                    ctx0, Qcur, inp_pos, nullptr,
 58                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 59                    ext_factor, attn_factor, beta_fast, beta_slow
 60                    );
 61
 62            Kcur = ggml_rope_ext(
 63                    ctx0, Kcur, inp_pos, nullptr,
 64                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 65                    ext_factor, attn_factor, beta_fast, beta_slow
 66                    );
 67
 68            cb(Qcur, "Qcur", il);
 69            cb(Kcur, "Kcur", il);
 70            cb(Vcur, "Vcur", il);
 71
 72            cur = build_attn(inp_attn,
 73                    model.layers[il].wo, model.layers[il].bo,
 74                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 75        }
 76        if (il == n_layer - 1 && inp_out_ids) {
 77            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
 78            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
 79        }
 80        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
 81        cb(ffn_inp, "ffn_inp", il);
 82
 83        // feed-forward network
 84        cur = build_norm(ffn_inp,
 85                model.layers[il].ffn_norm,
 86                model.layers[il].ffn_norm_b,
 87                LLM_NORM, il);
 88        cb(cur, "ffn_norm", il);
 89
 90        cur = build_ffn(cur,
 91                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
 92                NULL,                      NULL,                        NULL,
 93                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
 94                NULL,
 95                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
 96
 97        cur = ggml_add(ctx0, cur, ffn_inp);
 98        cb(cur, "ffn_out", il);
 99
100        cur = build_cvec(cur, il);
101        cb(cur, "l_out", il);
102
103        // input for next layer
104        inpL = cur;
105    }
106    cur = inpL;
107
108    cur = build_norm(cur,
109            model.output_norm, model.output_norm_b,
110            LLM_NORM, -1);
111
112    cb(cur, "result_norm", -1);
113    res->t_embd = cur;
114
115    // lm_head
116    cur = build_lora_mm(model.output, cur);
117
118    cb(cur, "result_output", -1);
119    res->t_logits = cur;
120
121    ggml_build_forward_expand(gf, cur);
122}