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
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
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266

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

ggml_cgraph * llm_build_context::build_bitnet() {
    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));

    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) {
        struct ggml_tensor * inpSA = inpL;

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

        // self-attention
        {
            // compute Q and K and RoPE them
            struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
            float q_scale; std::memcpy(&q_scale, model.layers[il].wq->op_params, sizeof(float));
            // Note: we could save this scale operation by applying the Q scale on the K * Q product further down
            // (which also uses a scale). This works on the CPU and Metal backends, but produces NaNs on CUDA.
            if (fabsf(q_scale-1) > 1e-4f) Qcur = ggml_scale(ctx0, Qcur, q_scale);
            cb(Qcur, "Qcur", il);
            if (model.layers[il].bq) {
                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                cb(Qcur, "Qcur", il);
            }

            // B1.K
            struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
            float k_scale; std::memcpy(&k_scale, model.layers[il].wk->op_params, sizeof(float));
            if (fabsf(k_scale-1) > 1e-4f) Kcur = ggml_scale(ctx0, Kcur, k_scale);
            cb(Kcur, "Kcur", il);
            if (model.layers[il].bk) {
                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                cb(Kcur, "Kcur", il);
            }

            // B1.V
            struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
            float v_scale; std::memcpy(&v_scale, model.layers[il].wv->op_params, sizeof(float));
            if (model.layers[il].bv) {
                if (fabsf(v_scale-1) > 1e-4f) Vcur = ggml_scale(ctx0, Vcur, v_scale);
                v_scale = 1;
                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
            }
            cb(Vcur, "Vcur", il);

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

            ggml_tensor * cur_attn = llm_build_kv(ctx0, lctx, kv_self, gf,
                    // we cannot pass model.layers[il].wo and model.layers[il].bo because we need to do rms_norm first
                    nullptr, nullptr,
                    Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);

            cur_attn = llm_build_norm(ctx0, cur_attn, hparams, model.layers[il].attn_sub_norm, NULL, LLM_NORM_RMS, cb, il, 1/(v_scale*v_scale));
            cb(cur_attn, "attn_sub_norm", il);

            ggml_build_forward_expand(gf, cur_attn);

            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur_attn);
            float wo_scale; std::memcpy(&wo_scale, model.layers[il].wo->op_params, sizeof(float));
            if (fabsf(wo_scale-1) > 1e-4f) cur = ggml_scale(ctx0, cur, wo_scale);

            cb(cur, "kqv_out", il);
        }

        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);
            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
        }

        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
        cb(ffn_inp, "ffn_inp", il);

        // feed-forward forward
        if (model.layers[il].ffn_gate_inp == nullptr) {
            cur = llm_build_norm(ctx0, ffn_inp, hparams, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
            float ffn_up_scale; std::memcpy(&ffn_up_scale, model.layers[il].ffn_up->op_params, sizeof(float));

            cb(tmp, "ffn_up", il);

            cur = ggml_mul_mat(ctx0, model.layers[il].ffn_gate, cur);
            float ffn_gate_scale; std::memcpy(&ffn_gate_scale, model.layers[il].ffn_gate->op_params, sizeof(float));
            if (fabsf(ffn_gate_scale-1) > 1e-4f) cur = ggml_scale(ctx0, cur, ffn_gate_scale);

            cb(cur, "ffn_gate", il);

            cur = ggml_fused_mul_unary(ctx0, cur, tmp, GGML_UNARY_OP_SILU);
            cb(cur, "ffn_gate_par", il);

            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_sub_norm, NULL, LLM_NORM_RMS, cb, il, 1/(ffn_up_scale*ffn_up_scale));
            cb(cur, "ffn_sub_norm", il);

            cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
            float ffn_down_scale; std::memcpy(&ffn_down_scale, model.layers[il].ffn_down->op_params, sizeof(float));
            if (fabsf(ffn_down_scale-1) > 1e-4f) cur = ggml_scale(ctx0, cur, ffn_down_scale);
            cb(cur, "ffn_down", il);
        }
        cur = ggml_add(ctx0, cur, ffn_inp);
        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;
}

ggml_cgraph * llm_build_context::build_bitnet_158() {
    ggml_cgraph * gf = new_graph_custom();

    // mutable variable, needed during the last layer of the computation to skip unused tokens
    int32_t n_tokens = this->n_tokens;

    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) {
        struct ggml_tensor * inpSA = inpL;

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

        // self-attention
        {
            // rope freq factors for llama3; may return nullptr for llama2 and other models
            struct ggml_tensor * rope_factors = build_rope_factors(il);

            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_embd_head, n_head, n_tokens), inp_pos, rope_factors,
                    n_rot, 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_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
                    n_rot, 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,
                    NULL, NULL,
                    Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);

            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_sub_norm, NULL, LLM_NORM_RMS, cb, il);
            cb(cur, "attn_sub_norm", il);

            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
            if (model.layers[il].wo_scale) {
                cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
            }
            if (model.layers[il].bo) {
                cur = ggml_add(ctx0, cur, model.layers[il].bo);
            }
            cb(cur, "attn_o_out", il);
        }

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

        struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
        cb(ffn_inp, "ffn_inp", il);

        cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
                model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_scale,
                model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale,
                NULL, NULL, NULL,
                NULL,
                LLM_FFN_RELU_SQR, LLM_FFN_PAR, cb, il);
        cb(cur, "ffn_out", il);

        cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_sub_norm, NULL, LLM_NORM_RMS, cb, il);
        cb(cur, "ffn_sub_norm", il);

        cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down, cur);
        if (model.layers[il].ffn_down_scale) {
            cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
        }
        cb(cur, "ffn_down", il);

        cur = ggml_add(ctx0, cur, ffn_inp);
        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;
}