1#include "models.h"
 2
 3
 4llm_build_mamba::llm_build_mamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
 5    ggml_tensor * cur;
 6    ggml_tensor * inpL;
 7
 8    // {n_embd, n_tokens}
 9    inpL = build_inp_embd(model.tok_embd);
10
11    auto * rs_inp = build_rs_inp();
12
13    ggml_tensor * inp_out_ids = build_inp_out_ids();
14
15    for (int il = 0; il < n_layer; ++il) {
16        // norm
17        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
18        cb(cur, "attn_norm", il);
19
20        if (model.arch == LLM_ARCH_MAMBA2) {
21            cur = build_mamba2_layer(rs_inp, cur, model, ubatch, il);
22        } else {
23            cur = build_mamba_layer(rs_inp, cur, model, ubatch, il);
24        }
25
26        if (il == n_layer - 1 && inp_out_ids) {
27            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);
28            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
29        }
30
31        // residual
32        cur = ggml_add(ctx0, cur, inpL);
33
34        cur = build_cvec(cur, il);
35        cb(cur, "l_out", il);
36
37        // input for next layer
38        inpL = cur;
39    }
40
41    // final rmsnorm
42    cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
43
44    cb(cur, "result_norm", -1);
45    res->t_embd = cur;
46
47    // lm_head
48    cur = build_lora_mm(model.output, cur);
49
50    cb(cur, "result_output", -1);
51    res->t_logits = cur;
52
53    ggml_build_forward_expand(gf, cur);
54}
55