char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

Georgi Gerganovmetal : single-source fusion table + fusion debug rework (#28164)a2878d30d

char/aliceai-t5-moe
brancheschar/aliceai-t5-moeall refs →
19.2 KiB484 linesraw
1#include "models.h"
2#include "llama-memory-recurrent.h"
3
4void llama_model_minimax_01::load_arch_hparams(llama_model_loader & ml) {
5    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
6    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale);
7
8    // we use n_embd_head_la to set recurrent memory n_embd_s
9    hparams.n_embd_head_la = hparams.n_embd_head_k_full;
10
11    // Mark recurrent layers (lightning attention layers).
12    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
13        uint32_t full_attn_interval = 8;
14        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
15        for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {
16            hparams.is_recr_impl[i] = (i < hparams.n_layer()) && ((i + 1) % full_attn_interval != 0);
17        }
18    }
19
20    switch (hparams.n_layer()) {
21        case 80: type = LLM_TYPE_456B; break;
22        default: type = LLM_TYPE_UNKNOWN;
23    }
24}
25
26void llama_model_minimax_01::load_arch_tensors(llama_model_loader &) {
27    LLAMA_LOAD_LOCALS;
28
29    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
30
31    // output
32    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
33    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
34
35    // if output is NULL, init from the input tok embed
36    if (output == NULL) {
37        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
38    }
39
40    for (int i = 0; i < n_layer; ++i) {
41        auto & layer = layers[i];
42
43        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
44
45        if (!hparams.is_recr(i)) {
46            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
47        } else {
48            layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd_head_k * n_head}, 0);
49            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd_head_k * n_head}, 0);
50            layer.wg = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
51        }
52        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
53
54        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
55
56        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
57        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
58        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
59        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
60    }
61}
62
63std::unique_ptr<llm_graph_context> llama_model_minimax_01::build_arch_graph(const llm_graph_params & params) const {
64    return std::make_unique<graph>(*this, params);
65}
66
67class llm_graph_input_la : public llm_graph_input_i {
68public:
69    llm_graph_input_la(const llama_hparams & hparams) : hparams(hparams) {}
70
71    void set_input(const llama_ubatch * ubatch) override {
72        // this operates on assumption that we have an equal ubatch split
73
74        const int64_t n_head = hparams.n_head();
75        const int32_t n_seqs = ubatch->n_seqs;
76        const int32_t n_seqs_unq = ubatch->n_seqs_unq;
77        const int32_t n_tokens = ubatch->n_tokens;
78        const int32_t n_seq_tokens = ubatch->n_seq_tokens;
79
80        std::vector<llama_pos> p0(n_seqs_unq);
81        std::fill(p0.begin(), p0.end(), std::numeric_limits<llama_pos>::max());
82
83        // get lowest token position in a ubatch for each stream
84        for (int i = 0; i < n_tokens; ++i) {
85            llama_seq_id seq_id = ubatch->seq_id[i][0];
86            int32_t seq_idx = ubatch->seq_idx[seq_id];
87            llama_pos pos = ubatch->pos[i];
88            if (p0[seq_idx] > pos) {
89                p0[seq_idx] = pos;
90            }
91        }
92
93        if (inp_slopes) {
94            GGML_ASSERT(ggml_backend_buffer_is_host(inp_slopes->buffer));
95
96            float * data = (float *) inp_slopes->data;
97
98            float start = powf(2, -powf(2, -(log2f(n_head) - 3)));
99            float ratio = start;
100
101            for (int h = 0; h < n_head; ++h) {
102                data[h] = start * powf(ratio, h);
103            }
104        }
105
106        if (inp_q_decay) {
107            GGML_ASSERT(ggml_backend_buffer_is_host(inp_q_decay->buffer));
108
109            float * slopes = (float *) inp_slopes->data;
110            float * data = (float *) inp_q_decay->data;
111
112            for (int s = 0; s < n_seqs; ++s) {
113                for (int i = 0; i < n_seq_tokens; ++i) {
114                    llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + i][0];
115                    int32_t seq_idx = ubatch->seq_idx[seq_id];
116                    llama_pos pos = ubatch->pos[s * n_seq_tokens + i];
117                    int pos_rel = pos - p0[seq_idx];
118
119                    for (int h = 0; h < n_head; ++h) {
120                        data[seq_idx * n_head * n_seq_tokens + i * n_head + h] = -slopes[h] * (pos_rel + 1);
121                    }
122                }
123            }
124        }
125
126        if (inp_k_decay) {
127            GGML_ASSERT(ggml_backend_buffer_is_host(inp_k_decay->buffer));
128
129            float * slopes = (float *) inp_slopes->data;
130            float * data = (float *) inp_k_decay->data;
131
132            for (int s = 0; s < n_seqs; ++s) {
133                for (int i = 0; i < n_seq_tokens; ++i) {
134                    llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + i][0];
135                    int32_t seq_idx = ubatch->seq_idx[seq_id];
136                    llama_pos pos = ubatch->pos[s * n_seq_tokens + i];
137                    int pos_rel = pos - p0[seq_idx];
138
139                    for (int h = 0; h < n_head; ++h) {
140                        data[seq_idx * n_head * n_seq_tokens + i * n_head + h] = -slopes[h] * (n_seq_tokens - pos_rel - 1);
141                    }
142                }
143            }
144        }
145
146        if (inp_diag_decay) {
147            GGML_ASSERT(ggml_backend_buffer_is_host(inp_diag_decay->buffer));
148
149            float * slopes = (float *) inp_slopes->data;
150            float * data = (float *) inp_diag_decay->data;
151
152            for (int s = 0; s < n_seqs; ++s) {
153                for (int h = 0; h < n_head; ++h) {
154                    for (int j = 0; j < n_seq_tokens; ++j) {
155                        llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + j][0];
156                        int32_t seq_idx = ubatch->seq_idx[seq_id];
157                        llama_pos pos_j = ubatch->pos[s * n_seq_tokens + j];
158                        int pos_rel_j = pos_j - p0[seq_idx];
159
160                        for (int i = 0; i < n_seq_tokens; ++i) {
161                            llama_pos pos_i = ubatch->pos[s * n_seq_tokens + i];
162                            int pos_rel_i = pos_i - p0[seq_idx];
163
164                            int index = pos_rel_j - pos_rel_i;
165                            float s_index = index >= 0 ? -slopes[h] * index : -INFINITY;
166                            data[seq_idx * n_head * n_seq_tokens * n_seq_tokens + h * n_seq_tokens * n_seq_tokens + j * n_seq_tokens + i] = s_index;
167                        }
168                    }
169                }
170            }
171        }
172    }
173
174    bool can_reuse(const llm_graph_params & params) override {
175        bool res = true;
176
177        res &= (   inp_q_decay &&    inp_q_decay->ne[2] == params.ubatch.n_seq_tokens);
178        res &= (   inp_k_decay &&    inp_k_decay->ne[2] == params.ubatch.n_seq_tokens);
179        res &= (inp_diag_decay && inp_diag_decay->ne[1] == params.ubatch.n_seq_tokens);
180
181        return res;
182    }
183
184    const llama_hparams hparams;
185
186    ggml_tensor * inp_slopes     = nullptr; // F32 [n_head]
187    ggml_tensor * inp_q_decay    = nullptr; // F32 [1, n_head, n_batch]
188    ggml_tensor * inp_k_decay    = nullptr; // F32 [1, n_head, n_batch]
189    ggml_tensor * inp_diag_decay = nullptr; // F32 [n_batch, n_batch, n_head]
190};
191
192llama_model_minimax_01::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
193    const int64_t n_embd_head = hparams.n_embd_head_v();
194
195    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
196    // GGML_ASSERT(n_embd_head == n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
197
198    const int64_t n_seqs  = ubatch.n_seqs;
199    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
200
201    GGML_ASSERT(n_seqs != 0);
202    GGML_ASSERT(ubatch.equal_seqs());
203    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
204
205    ggml_tensor * cur;
206    ggml_tensor * inpL;
207
208    inpL = build_inp_embd(model.tok_embd);
209
210    auto * inp_hybrid = build_inp_mem_hybrid();
211    auto * inp_rs = inp_hybrid->get_recr();
212
213    ggml_tensor * inp_pos = build_inp_pos();
214    ggml_tensor * inp_out_ids = build_inp_out_ids();
215
216    llm_graph_input_la * la = nullptr;
217
218    auto inp = std::make_unique<llm_graph_input_la>(hparams);
219
220    inp->inp_slopes = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_head);
221    ggml_set_input(inp->inp_slopes);
222    cb(inp->inp_slopes, "slopes", -1);
223
224    inp->inp_q_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_head, n_seq_tokens, n_seqs);
225    ggml_set_input(inp->inp_q_decay);
226    cb(inp->inp_q_decay, "q_decay_exp", -1);
227
228    inp->inp_k_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_head, n_seq_tokens, n_seqs);
229    ggml_set_input(inp->inp_k_decay);
230    cb(inp->inp_k_decay, "k_decay_exp", -1);
231
232    // [TAG_RESERVE_DIAG_DECAY]
233    inp->inp_diag_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_seq_tokens, n_seq_tokens, n_head, n_seqs);
234    ggml_set_input(inp->inp_diag_decay);
235    cb(inp->inp_diag_decay, "diag_decay_exp", -1);
236
237    la = (llm_graph_input_la *) res->add_input(std::move(inp));
238
239    ggml_tensor * slopes = la->inp_slopes;
240
241    for (int il = 0; il < n_layer; ++il) {
242        res->t_layer_inp[il] = inpL;
243
244        ggml_tensor * inpSA = inpL;
245
246        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
247        cb(cur, "attn_norm", il);
248
249        ggml_tensor * residual = cur;
250
251        // self_attention
252        if (!hparams.is_recr(il)) {
253            // softmax attention layer
254
255            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
256                    n_embd_head, n_head, n_head_kv, il);
257
258            Qcur = ggml_rope_ext(
259                ctx0, Qcur, inp_pos, nullptr,
260                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
261                ext_factor, attn_factor, beta_fast, beta_slow
262                );
263
264            Kcur = ggml_rope_ext(
265                ctx0, Kcur, inp_pos, nullptr,
266                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
267                ext_factor, attn_factor, beta_fast, beta_slow
268                );
269
270            cb(Qcur, "Qcur", il);
271            cb(Kcur, "Kcur", il);
272            cb(Vcur, "Vcur", il);
273
274            cur = build_attn(inp_hybrid->get_attn(),
275                    model.layers[il].wo, NULL, model.layers[il].wo_s,
276                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
277        } else {
278            // lightning attention layer
279
280            const auto * mctx_cur = inp_rs->mctx;
281            const auto kv_head = mctx_cur->get_head();
282
283            // TODO unneeded - any way to make conv states optional in recurrent memory?
284            ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
285            ggml_tensor * conv_state_all  = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
286            ggml_build_forward_expand(gf, conv_state_all);
287
288            float slope_scale = 1.0 - 1.0 * il / (n_layer - 1) + 1e-5;
289            ggml_tensor * slope_rate = ggml_scale(ctx0, slopes, slope_scale);
290            cb(slope_rate, "slope_rate", il);
291
292            cur = ggml_reshape_4d(ctx0, cur, cur->ne[0], n_seq_tokens, 1, n_seqs);
293
294            ggml_tensor * QKVcur = build_lora_mm(model.layers[il].wqkv, cur);
295            cb(QKVcur, "QKVcur", il);
296
297            QKVcur = ggml_silu(ctx0, QKVcur);
298            cb(QKVcur, "QKVcur_silu", il);
299
300            QKVcur = ggml_reshape_4d(ctx0, QKVcur, n_embd_head * 3, n_head, n_seq_tokens, n_seqs);
301
302            ggml_tensor * Qcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 0*ggml_element_size(QKVcur)*n_embd_head);
303            ggml_tensor * Kcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 1*ggml_element_size(QKVcur)*n_embd_head);
304            ggml_tensor * Vcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 2*ggml_element_size(QKVcur)*n_embd_head);
305
306            cb(Qcur, "Qcur", il);
307            cb(Kcur, "Kcur", il);
308            cb(Vcur, "Vcur", il);
309
310            // get previous KV
311            ggml_tensor * la_states_all = mctx_cur->get_s_l(il);
312            ggml_tensor * state = build_rs(inp_rs, la_states_all, hparams.n_embd_s(), n_seqs);
313
314            ggml_tensor * kv_old = ggml_reshape_4d(ctx0, state, n_embd_head, n_embd_head, n_head, n_seqs);
315            cb(kv_old, "kv_old", il);
316
317            ggml_tensor * qkv = nullptr;
318            ggml_tensor * kv_new = nullptr;
319            {
320                // lightning attention
321
322                ggml_tensor *    q_decay_exp = la->inp_q_decay;
323                ggml_tensor *    k_decay_exp = la->inp_k_decay;
324                ggml_tensor * diag_decay_exp = la->inp_diag_decay;
325
326                ggml_tensor *    q_decay = ggml_exp(ctx0, ggml_scale(ctx0, q_decay_exp, slope_scale));
327                cb(q_decay, "q_decay", il);
328                ggml_tensor *    k_decay = ggml_exp(ctx0, ggml_scale(ctx0, k_decay_exp, slope_scale));
329                cb(k_decay, "k_decay", il);
330                ggml_tensor * diag_decay = ggml_exp(ctx0, ggml_scale(ctx0, diag_decay_exp, slope_scale));
331                cb(diag_decay, "diag_decay", il);
332
333                ggml_tensor * q_s = ggml_mul(ctx0, Qcur, q_decay);
334                cb(q_s, "q_s", il);
335
336                ggml_tensor * q_s_trans = ggml_permute(ctx0, q_s, 0, 2, 1, 3);
337                cb(q_s_trans, "q_s_trans", il);
338
339                ggml_tensor * qkv_none_diag = ggml_mul_mat(ctx0, kv_old, q_s_trans);
340                cb(qkv_none_diag, "qkv_none_diag", il);
341
342                ggml_tensor * q_trans = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
343                cb(q_trans, "q_trans", il);
344
345                ggml_tensor * k_trans = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
346                cb(k_trans, "k_trans", il);
347
348                ggml_tensor * qk = ggml_mul_mat(ctx0, k_trans, q_trans);
349                cb(qk, "qk", il);
350
351                qk = ggml_mul(ctx0, qk, diag_decay);
352                cb(qk, "qk_s", il);
353
354                ggml_tensor * v_trans = ggml_cont(ctx0, ggml_permute(ctx0, Vcur, 1, 2, 0, 3));
355                cb(v_trans, "v_trans", il);
356
357                ggml_tensor * qkv_diag = ggml_mul_mat(ctx0, v_trans, qk);
358                cb(qkv_diag, "qkv_diag", il);
359
360                qkv = ggml_add(ctx0, qkv_none_diag, qkv_diag);
361                cb(qkv, "qkv", il);
362
363                ggml_build_forward_expand(gf, qkv);
364
365                ggml_tensor * slopes_neg = ggml_scale(ctx0, slope_rate, -1.0*n_seq_tokens);
366                cb(slopes_neg, "slopes_neg", il);
367
368                ggml_tensor * block_decay = ggml_exp(ctx0, slopes_neg);
369                cb(block_decay, "block_decay", il);
370
371                ggml_tensor * block_decay_3d = ggml_reshape_3d(ctx0, block_decay, 1, 1, n_head);
372                cb(block_decay_3d, "block_decay_3d", il);
373
374                ggml_tensor * kv_old_s = ggml_mul(ctx0, kv_old, block_decay_3d);
375                cb(kv_old_s, "kv_old_s", il);
376
377                ggml_tensor * k_after_decay = ggml_mul(ctx0, Kcur, k_decay);
378                cb(k_after_decay, "k_after_decay", il);
379
380                ggml_tensor * k_after_decay_trans = ggml_cont(ctx0, ggml_permute(ctx0, k_after_decay, 1, 2, 0, 3));
381                cb(k_after_decay_trans, "k_after_decay_trans", il);
382
383                ggml_tensor * kv_cur = ggml_mul_mat(ctx0, k_after_decay_trans, v_trans);
384                cb(kv_cur, "kv_cur", il);
385
386                kv_new = ggml_add(ctx0, kv_old_s, kv_cur);
387                cb(kv_new, "kv_new", il);
388            }
389
390            // store new KV
391            ggml_build_forward_expand(gf,
392                                     ggml_cpy(ctx0, kv_new,
393                                              ggml_view_1d(ctx0, la_states_all, hparams.n_embd_s() * n_seqs,
394                                                           kv_head * hparams.n_embd_s() * ggml_element_size(la_states_all))));
395
396            qkv = ggml_cont(ctx0, ggml_permute(ctx0, qkv, 0, 2, 1, 3));
397            cb(qkv, "qkv_permuted", il);
398
399            qkv = ggml_reshape_4d(ctx0, qkv, qkv->ne[0]*qkv->ne[1], qkv->ne[2], 1, qkv->ne[3]);
400
401            // norm
402            ggml_tensor * qkv_norm = build_norm(qkv,
403                    model.layers[il].attn_norm_2, NULL,
404                    LLM_NORM_RMS, il);
405            cb(qkv_norm, "qkv_norm", il);
406
407            ggml_tensor * g = build_lora_mm(model.layers[il].wg, cur);
408            cb(g, "g", il);
409
410            g = ggml_sigmoid(ctx0, g);
411            cb(g, "g_sigm", il);
412
413            cur = ggml_mul(ctx0, g, qkv_norm);
414
415            cur = build_lora_mm(model.layers[il].wo, cur);
416            cb(cur, "attn_out", il);
417
418            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens*n_seqs);
419            cb(cur, "attn_out", il);
420        }
421
422        if (il == n_layer - 1 && inp_out_ids) {
423            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
424            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
425            residual = ggml_get_rows(ctx0, residual, inp_out_ids);
426        }
427
428        residual = ggml_scale(ctx0, residual, hparams.f_residual_scale);
429        cb(residual, "residual_scaled_attn", il);
430
431        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, residual);
432        cb(ffn_inp, "ffn_inp", il);
433
434        // MoE branch
435        cur = build_norm(ffn_inp,
436                model.layers[il].ffn_norm, NULL,
437                LLM_NORM_RMS, il);
438        cb(cur, "ffn_norm", il);
439
440        residual = cur;
441
442        cur = build_moe_ffn(cur,
443                model.layers[il].ffn_gate_inp,
444                model.layers[il].ffn_up_exps,
445                model.layers[il].ffn_gate_exps,
446                model.layers[il].ffn_down_exps,
447                model.layers[il].ffn_exp_probs_b,
448                n_expert, n_expert_used,
449                LLM_FFN_SILU, true,
450                hparams.expert_weights_scale,
451                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
452                il);
453        cb(cur, "ffn_moe_out", il);
454
455        residual = ggml_scale(ctx0, residual, hparams.f_residual_scale);
456        cb(residual, "residual_scaled_ffn", il);
457
458        cur = ggml_add(ctx0, cur, residual);
459        cb(cur, "ffn_out", il);
460
461        cur = build_cvec(cur, il);
462        cb(cur, "l_out", il);
463
464        // input for next layer
465        inpL = cur;
466    }
467
468    cur = inpL;
469
470    cur = build_norm(cur,
471            model.output_norm, NULL,
472            LLM_NORM_RMS, -1);
473
474    cb(cur, "result_norm", -1);
475    res->t_embd = cur;
476
477    // lm_head
478    cur = build_lora_mm(model.output, cur, model.output_s);
479
480    cb(cur, "result_output", -1);
481    res->t_logits = cur;
482
483    ggml_build_forward_expand(gf, cur);
484}