slm_ikllama_sys 0.1.1

ik_llama.cpp rust sys bindings
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97

#include "../llama-build-context.h"
#include "../llama-model.h"
#include "../llama-context.h"

ggml_cgraph * llm_build_context::build_plamo() {
    ggml_cgraph * gf = new_graph_custom();

    const int64_t n_embd_head = hparams.n_embd_head_v(0);
    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
    GGML_ASSERT(n_embd_head == hparams.n_rot);

    struct ggml_tensor * cur;
    struct ggml_tensor * inpL;

    inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

    // inp_pos - contains the positions
    struct ggml_tensor * inp_pos = build_inp_pos();

    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
    struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

    for (int il = 0; il < n_layer; ++il) {

        // norm
        cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
        cb(cur, "attn_norm", il);

        struct ggml_tensor * attention_norm = cur;

        // self-attention
        {
            auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, model.layers[il].wq, nullptr,
                    model.layers[il].wk, nullptr,
                    model.layers[il].wv, nullptr, 0, il);
            Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos, nullptr,
                    n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow);
            cb(Qcur, "Qcur", il);

            Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow);
            cb(Kcur, "Kcur", il);

            cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                    model.layers[il].wo, NULL,
                    Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
        }
        struct ggml_tensor * sa_out = cur;

        cur = attention_norm;

        if (il == n_layer - 1) {
            // skip computing output for unused tokens
            struct ggml_tensor * inp_out_ids = build_inp_out_ids();
            cur    = ggml_get_rows(ctx0,    cur, inp_out_ids);
            sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
            inpL   = ggml_get_rows(ctx0,   inpL, inp_out_ids);
        }

        // feed-forward network
        {
            cur = llm_build_ffn(ctx0, lctx, nullptr, cur,
                    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, cb, il);
            cb(cur, "ffn_out", il);
        }

        cur = ggml_add(ctx0, cur, sa_out);
        cur = ggml_add(ctx0, cur, inpL);
        cur = lctx.cvec.apply_to(ctx0, cur, il);
        cb(cur, "l_out", il);

        // input for next layer
        inpL = cur;
    }

    cur = inpL;

    cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
    cb(cur, "result_norm", -1);

    // lm_head
    cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
    cb(cur, "result_output", -1);

    ggml_build_forward_expand(gf, cur);

    return gf;
}