File size: 4,725 Bytes
2517be1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
682b227
2517be1
 
 
 
 
 
 
 
 
 
682b227
2517be1
 
 
682b227
2517be1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
682b227
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2517be1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
#include "models.h"

ggml_cgraph * clip_graph_whisper_enc::build() {
    const int n_frames = img.nx;
    const int n_pos    = n_frames / 2;
    GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos);

    ggml_tensor * inp = build_inp_raw(1);

    // conv1d block
    {
        // convolution + gelu
        ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1);
        cur = ggml_add(ctx0, cur, model.conv1d_1_b);

        cur = ggml_gelu_erf(ctx0, cur);

        cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1);
        cur = ggml_add(ctx0, cur, model.conv1d_2_b);

        cur = ggml_gelu_erf(ctx0, cur);
        // transpose
        inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
        cb(inp, "after_conv1d", -1);
    }

    // sanity check (only check one layer, but it should be the same for all)
    GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b);
    GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b);
    GGML_ASSERT(model.layers[0].q_b);
    GGML_ASSERT(model.layers[0].v_b);
    GGML_ASSERT(!model.layers[0].k_b); // no bias for k

    ggml_tensor * pos_embd_selected = ggml_view_2d(
        ctx0, model.position_embeddings,
        model.position_embeddings->ne[0], n_pos,
        model.position_embeddings->nb[1], 0
    );
    ggml_tensor * cur = build_vit(
                            inp, n_pos,
                            NORM_TYPE_NORMAL,
                            hparams.ffn_op,
                            pos_embd_selected,
                            nullptr);

    cb(cur, "after_transformer", -1);

    if (model.audio_has_stack_frames()) {
        // StackAudioFrames
        // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py
        cur = build_stack(cur, hparams.proj_stack_factor, n_embd);
        cb(cur, "after_stacked", -1);
    }

    if (proj_type == PROJECTOR_TYPE_ULTRAVOX) {
        // UltravoxProjector
        // pre-norm
        cur = ggml_rms_norm(ctx0, cur, 1e-6);
        cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);

        // ffn in
        cur = build_mm(model.mm_1_w, cur);

        // swiglu
        // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half
        cur = ggml_swiglu_swapped(ctx0, cur);

        // mid-norm
        cur = ggml_rms_norm(ctx0, cur, 1e-6);
        cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w);

        // ffn out
        cur = build_mm(model.mm_2_w, cur);

    } else if (proj_type == PROJECTOR_TYPE_QWEN2A) {
        // projector
        cur = build_mm(model.mm_fc_w, cur);
        cur = ggml_add(ctx0, cur, model.mm_fc_b);

    } else if (proj_type == PROJECTOR_TYPE_VOXTRAL) {
        // projector
        cur = build_ffn(cur,
            model.mm_1_w, model.mm_1_b,
            nullptr, nullptr,
            model.mm_2_w, model.mm_2_b,
            FFN_GELU_ERF,
            -1);

    } else if (proj_type == PROJECTOR_TYPE_MUSIC_FLAMINGO) {
        // projector
        cur = build_ffn(cur,
            model.mm_1_w, model.mm_1_b,
            nullptr, nullptr,
            model.mm_2_w, model.mm_2_b,
            FFN_GELU_ERF,
            -1);

    } else if (proj_type == PROJECTOR_TYPE_MERALION) {
        // stack (above) -> ln -> linear0+silu -> GLU -> out
        cur = ggml_norm(ctx0, cur, hparams.eps);
        cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
        cur = ggml_add(ctx0, cur, model.mm_norm_pre_b);

        cur = ggml_mul_mat(ctx0, model.mm_0_w, cur);
        cur = ggml_add(ctx0, cur, model.mm_0_b);
        cur = ggml_silu(ctx0, cur);

        ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_1_w, cur);
        gate = ggml_add(ctx0, gate, model.mm_1_b);
        gate = ggml_silu(ctx0, gate);

        ggml_tensor * pool = ggml_mul_mat(ctx0, model.mm_2_w, cur);
        pool = ggml_add(ctx0, pool, model.mm_2_b);

        cur = ggml_mul(ctx0, gate, pool);

        cur = ggml_mul_mat(ctx0, model.mm_3_w, cur);
        cur = ggml_add(ctx0, cur, model.mm_3_b);

    } else if (proj_type == PROJECTOR_TYPE_GLMA) {
            cur = ggml_norm(ctx0, cur, hparams.eps);
            cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
            cur = ggml_add(ctx0, cur, model.mm_norm_pre_b);
            cur = build_stack(cur, hparams.proj_stack_factor, n_embd);
            cur = build_ffn(cur, model.mm_1_w, model.mm_1_b, nullptr, nullptr, model.mm_2_w, model.mm_2_b, hparams.ffn_op, 0);
            cur = ggml_concat(ctx0, model.mm_boi, cur, 1);
            cur = ggml_concat(ctx0, cur, model.mm_eoi, 1);
    } else {
        GGML_ABORT("%s: unknown projector type", __func__);
    }

    cb(cur, "projected", -1);

    ggml_build_forward_expand(gf, cur);

    return gf;
}