1#include "models.h"
  2
  3
  4llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
  5    const int64_t n_embd_head = hparams.n_embd_head_v;
  6    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
  7
  8    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
  9    GGML_ASSERT(n_embd_head == hparams.n_rot);
 10
 11    ggml_tensor * cur;
 12    ggml_tensor * inpL;
 13
 14    inpL = build_inp_embd(model.tok_embd);
 15
 16    // inp_pos - contains the positions
 17    ggml_tensor * inp_pos = build_inp_pos();
 18
 19    auto * inp_attn = build_attn_inp_kv();
 20
 21    ggml_tensor * inp_out_ids = build_inp_out_ids();
 22
 23    for (int il = 0; il < n_layer; ++il) {
 24        ggml_tensor * attn_norm;
 25
 26        attn_norm = build_norm(inpL,
 27                model.layers[il].attn_norm,
 28                model.layers[il].attn_norm_b,
 29                LLM_NORM, il);
 30        cb(attn_norm, "attn_norm", il);
 31
 32        // self-attention
 33        {
 34            if (model.layers[il].attn_norm_2) {
 35                // Falcon-40B
 36                cur = build_norm(inpL,
 37                        model.layers[il].attn_norm_2,
 38                        model.layers[il].attn_norm_2_b,
 39                        LLM_NORM, il);
 40                cb(cur, "attn_norm_2", il);
 41            } else {
 42                cur = attn_norm;
 43            }
 44
 45            cur = build_lora_mm(model.layers[il].wqkv, cur);
 46            cb(cur, "wqkv", il);
 47
 48            ggml_tensor * 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));
 49            ggml_tensor * 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));
 50            ggml_tensor * 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));
 51
 52            // using mode = 2 for neox mode
 53            Qcur = ggml_rope_ext(
 54                    ctx0, Qcur, inp_pos, nullptr,
 55                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 56                    ext_factor, attn_factor, beta_fast, beta_slow
 57                    );
 58
 59            Kcur = ggml_rope_ext(
 60                    ctx0, Kcur, inp_pos, nullptr,
 61                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
 62                    ext_factor, attn_factor, beta_fast, beta_slow
 63                    );
 64
 65            cb(Qcur, "Qcur", il);
 66            cb(Kcur, "Kcur", il);
 67            cb(Vcur, "Vcur", il);
 68
 69            cur = build_attn(inp_attn,
 70                    model.layers[il].wo, NULL,
 71                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
 72        }
 73
 74        if (il == n_layer - 1 && inp_out_ids) {
 75            cur       = ggml_get_rows(ctx0,       cur, inp_out_ids);
 76            inpL      = ggml_get_rows(ctx0,      inpL, inp_out_ids);
 77            attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
 78        }
 79
 80        ggml_tensor * ffn_inp = cur;
 81
 82        // feed forward
 83        {
 84            cur = build_ffn(attn_norm, // !! use the attn norm, not the result
 85                    model.layers[il].ffn_up,   NULL, NULL,
 86                    NULL,                      NULL, NULL,
 87                    model.layers[il].ffn_down, NULL, NULL,
 88                    NULL,
 89                    LLM_FFN_GELU, LLM_FFN_SEQ, il);
 90            cb(cur, "ffn_out", il);
 91        }
 92
 93        cur = ggml_add(ctx0, cur, ffn_inp);
 94        cur = ggml_add(ctx0, cur, inpL);
 95
 96        cur = build_cvec(cur, il);
 97        cb(cur, "l_out", il);
 98
 99        // input for next layer
100        inpL = cur;
101    }
102
103    cur = inpL;
104
105    // norm
106    cur = build_norm(cur,
107            model.output_norm,
108            model.output_norm_b,
109            LLM_NORM, -1);
110
111    cb(cur, "result_norm", -1);
112    res->t_embd = cur;
113
114    cur = build_lora_mm(model.output, cur);
115
116    cb(cur, "result_output", -1);
117    res->t_logits = cur;
118
119    ggml_build_forward_expand(gf, cur);
120}