char-fork/llama.cpp

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

Yaniss Amazouzmodel: add NVIDIA Nemotron-3-Puzzle-75B-A9B (NemotronHPuzzle) support (#25444)c61b98b87

char/aliceai-t5-moe
brancheschar/aliceai-t5-moeall refs →
18.7 KiB444 linesraw
1#include "models.h"
2
3void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
4    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
5    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
6    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
7
8    // MoE parameters
9    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
10    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
11    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
12    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
13
14    // Expert gating function (GLM-4.5 uses sigmoid)
15    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
16    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
17        hparams.expert_gating_func =  LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
18    }
19
20    switch (hparams.n_layer()) {
21        case 46: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air
22        case 48: type = LLM_TYPE_102B_A12B; break; // Solar Open
23        case 92: type = LLM_TYPE_355B_A32B; break; // GLM-4.5
24        default: type = LLM_TYPE_UNKNOWN;
25    }
26}
27
28void llama_model_glm4_moe::load_arch_tensors(llama_model_loader & ml) {
29    LLAMA_LOAD_LOCALS;
30    const int64_t n_expert_shared = hparams.n_expert_shared;
31
32    const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
33    const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
34    const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
35    const int trunk_flags = mtp_only  ? TENSOR_NOT_REQUIRED : 0;
36    int       mtp_flags   = trunk_only ? TENSOR_NOT_REQUIRED : 0;
37
38    if (!ml.load_mtp) {
39        mtp_flags |= TENSOR_SKIP;
40    }
41
42    GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
43    GGML_ASSERT(hparams.n_expert_used() > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
44
45    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
46
47    // output
48    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
49    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
50    // if output is NULL, init from the input tok embed
51    if (output == NULL) {
52        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
53    }
54
55    for (int i = 0; i < n_layer_all; ++i) {
56        auto & layer = layers[i];
57        const int flags = i < n_layer ? trunk_flags : mtp_flags;
58
59        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags);
60
61        // GLM-style attention with bias terms
62        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
63
64        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
65
66        // K/Q norm tensors (optional for GLM-4.5 355B variant)
67        layer.attn_q_norm = create_tensor(
68            tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
69        layer.attn_k_norm = create_tensor(
70            tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, TENSOR_NOT_REQUIRED | flags);
71
72        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, flags);
73
74        // Check if this layer uses MoE or dense FFN based on n_layer_dense_lead
75        // GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE
76        const bool use_moe = (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead);
77
78        if (use_moe) {
79            // MoE layers
80            layer.ffn_gate_inp =
81                create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
82            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags);
83
84            // MoE branch
85            const int64_t n_ff_exp = hparams.n_ff_exp() ? hparams.n_ff_exp() : n_ff / n_expert_used;
86
87            layer.ffn_gate_exps = create_tensor(
88                tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
89            layer.ffn_down_exps = create_tensor(
90                tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
91            layer.ffn_up_exps = create_tensor(
92                tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
93
94            // Shared expert
95            if (n_expert_shared > 0) {
96                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
97                layer.ffn_gate_shexp = create_tensor(
98                    tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
99                layer.ffn_down_shexp = create_tensor(
100                    tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
101                layer.ffn_up_shexp = create_tensor(
102                    tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
103            }
104        } else {
105            // Dense layers (first k layers) - GLM uses separate gate/up projections
106            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
107            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
108            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, flags);
109        }
110
111        // NextN/MTP tensors
112        if (i >= n_layer) {
113            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
114            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
115            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
116
117            // Optional tensors
118            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
119            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags);
120            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags);
121        }
122    }
123}
124
125std::unique_ptr<llm_graph_context> llama_model_glm4_moe::build_arch_graph(const llm_graph_params & params) const {
126    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
127        return std::make_unique<graph_mtp>(*this, params);
128    }
129    return std::make_unique<graph>(*this, params);
130}
131
132llama_model_glm4_moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
133    : llm_graph_context(params) {
134    GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4_MOE MTP requires n_layer_nextn > 0");
135    GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4_MOE MTP currently only supports a single MTP block");
136
137    const int64_t n_embd_head = hparams.n_embd_head_v();
138    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
139
140    const int il = hparams.n_layer() + cparams.nextn_layer_offset;
141    GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
142                cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
143                "nextn_layer_offset out of range [0, n_layer_nextn)");
144
145    const auto & layer = model.layers[il];
146
147    GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
148    GGML_ASSERT(layer.nextn.enorm   && "MTP block missing nextn.enorm");
149    GGML_ASSERT(layer.nextn.hnorm   && "MTP block missing nextn.hnorm");
150    GGML_ASSERT(layer.ffn_gate_inp  && "MTP block missing ffn_gate_inp");
151
152    auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
153
154    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
155    ggml_set_input(inp->tokens);
156
157    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
158    ggml_set_input(inp->embd);
159
160    ggml_tensor * tok_embd;
161    if (ubatch.token) {
162        ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
163        tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
164    } else {
165        tok_embd = inp->embd;
166    }
167    cb(tok_embd, "mtp_tok_embd", il);
168
169    inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
170    ggml_set_input(inp->h);
171    ggml_set_name(inp->h, "mtp_h_input");
172
173    ggml_tensor * h_embd = inp->h;
174
175    res->add_input(std::move(inp));
176
177    ggml_tensor * inp_pos     = build_inp_pos();
178    ggml_tensor * inp_out_ids = build_inp_out_ids();
179
180    auto * inp_attn = build_attn_inp_kv();
181
182    ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
183    cb(h_norm, "mtp_hnorm", il);
184
185    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
186    cb(e_norm, "mtp_enorm", il);
187
188    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0);
189    cb(concat, "mtp_concat", il);
190
191    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
192    cb(cur, "mtp_eh_proj", il);
193
194    ggml_tensor * inpSA = cur;
195
196    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
197    cb(cur, "mtp_attn_norm", il);
198
199    auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur,
200            n_embd_head, n_head, n_head_kv, il);
201
202    if (layer.attn_q_norm) {
203        Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il);
204        cb(Qcur, "mtp_Qcur_normed", il);
205    }
206    if (layer.attn_k_norm) {
207        Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il);
208        cb(Kcur, "mtp_Kcur_normed", il);
209    }
210
211    Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
212            rope_type, n_ctx_orig, freq_base, freq_scale,
213            ext_factor, attn_factor, beta_fast, beta_slow);
214
215    Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
216            rope_type, n_ctx_orig, freq_base, freq_scale,
217            ext_factor, attn_factor, beta_fast, beta_slow);
218
219    cb(Qcur, "mtp_Qcur", il);
220    cb(Kcur, "mtp_Kcur", il);
221    cb(Vcur, "mtp_Vcur", il);
222
223    cur = build_attn(inp_attn,
224            layer.wo, nullptr, layer.wo_s,
225            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
226            1.0f / sqrtf(float(n_embd_head)), il);
227    cb(cur, "mtp_attn_out", il);
228
229    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
230    cb(ffn_inp, "mtp_ffn_inp", il);
231
232    cur = build_norm(ffn_inp, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il);
233    cb(cur, "mtp_post_attn_norm", il);
234
235    ggml_tensor * routed_out = build_moe_ffn(cur,
236            layer.ffn_gate_inp,
237            layer.ffn_up_exps,
238            layer.ffn_gate_exps,
239            layer.ffn_down_exps,
240            layer.ffn_exp_probs_b,
241            n_expert, n_expert_used,
242            LLM_FFN_SILU, hparams.expert_weights_norm,
243            hparams.expert_weights_scale,
244            (llama_expert_gating_func_type) hparams.expert_gating_func,
245            il);
246    cb(routed_out, "mtp_ffn_moe_out", il);
247
248    ggml_tensor * shared_out = build_ffn(cur,
249            layer.ffn_up_shexp,   nullptr, nullptr,
250            layer.ffn_gate_shexp, nullptr, nullptr,
251            layer.ffn_down_shexp, nullptr, nullptr,
252            nullptr,
253            LLM_FFN_SILU, LLM_FFN_PAR, il);
254    cb(shared_out, "mtp_ffn_shexp_out", il);
255
256    cur = ggml_add(ctx0, routed_out, shared_out);
257    cb(cur, "mtp_ffn_out", il);
258
259    cur = ggml_add(ctx0, cur, ffn_inp);
260    cb(cur, "mtp_post_ffn", il);
261
262    ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
263            ? layer.nextn.shared_head_norm
264            : model.output_norm;
265    GGML_ASSERT(head_norm_w && "GLM4_MOE MTP: missing both nextn.shared_head_norm and output_norm");
266
267    cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
268    cb(cur, "h_nextn", -1);
269    res->t_h_nextn = cur;
270
271    if (inp_out_ids) {
272        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
273    }
274    cb(cur, "mtp_shared_head_norm", -1);
275
276    ggml_tensor * head_w = layer.nextn.shared_head_head
277            ? layer.nextn.shared_head_head
278            : model.output;
279    ggml_tensor * head_s = layer.nextn.shared_head_head
280            ? layer.nextn.shared_head_head_s
281            : model.output_s;
282    GGML_ASSERT(head_w && "GLM4_MOE MTP: missing LM head (nextn.shared_head_head or model.output)");
283
284    cur = build_lora_mm(head_w, cur, head_s);
285    cb(cur, "result_output", -1);
286
287    res->t_logits = cur;
288    ggml_build_forward_expand(gf, cur);
289}
290
291llama_model_glm4_moe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
292    const int64_t n_embd_head = hparams.n_embd_head_v();
293
294    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
295
296    int sections[4];
297    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
298
299    ggml_tensor * cur;
300    ggml_tensor * inpL;
301
302    inpL = build_inp_embd(model.tok_embd);
303
304    bool use_mrope = hparams.use_mrope();
305    if (ubatch.embd && !use_mrope) {
306        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
307        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
308    }
309
310    // inp_pos - contains the positions
311    ggml_tensor * inp_pos = build_inp_pos();
312
313    auto * inp_attn = build_attn_inp_kv();
314
315    ggml_tensor * inp_out_ids = build_inp_out_ids();
316
317    // NextN layers are processed by graph_mtp.
318    for (int il = 0; il < n_layer; ++il) {
319        ggml_tensor * inpSA = inpL;
320
321        // Pre-attention norm
322        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
323        cb(cur, "attn_norm", il);
324
325        // self-attention
326        {
327            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
328                    n_embd_head, n_head, n_head_kv, il);
329
330            // Apply Q/K norm if available (GLM-4.5 355B variant)
331            if (model.layers[il].attn_q_norm) {
332                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
333                cb(Qcur, "Qcur_normed", il);
334            }
335            if (model.layers[il].attn_k_norm) {
336                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
337                cb(Kcur, "Kcur_normed", il);
338            }
339
340            if (use_mrope) {
341                Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
342                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
343                            ext_factor, attn_factor, beta_fast, beta_slow);
344
345                Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
346                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
347                            ext_factor, attn_factor, beta_fast, beta_slow);
348            } else {
349                // Normal RoPE
350                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
351                                    rope_type, n_ctx_orig, freq_base, freq_scale,
352                                    ext_factor, attn_factor, beta_fast, beta_slow);
353
354                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
355                                    rope_type, n_ctx_orig, freq_base, freq_scale,
356                                    ext_factor, attn_factor, beta_fast, beta_slow);
357            }
358
359            cb(Qcur, "Qcur", il);
360            cb(Kcur, "Kcur", il);
361            cb(Vcur, "Vcur", il);
362
363            cur = build_attn(inp_attn,
364                    model.layers[il].wo, NULL, model.layers[il].wo_s,
365                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
366        }
367        if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
368            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
369            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
370        }
371        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
372        cb(ffn_inp, "ffn_inp", il);
373
374        // Post-attention norm
375        cur = build_norm(ffn_inp, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
376        cb(cur, "post_attn_norm", il);
377
378        // Check if this is a dense layer (n_layer_dense_lead=1, so layer 0 is dense)
379        if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
380            // Dense FFN layer
381            cur = build_ffn(cur,
382                    model.layers[il].ffn_up,   NULL, NULL,
383                    model.layers[il].ffn_gate, NULL, NULL,
384                    model.layers[il].ffn_down, NULL, NULL,
385                    NULL,
386                    LLM_FFN_SILU, LLM_FFN_PAR, il);
387            cb(cur, "ffn_out", il);
388        } else {
389            // Process routed experts using existing MoE infrastructure
390            ggml_tensor * routed_out = build_moe_ffn(cur,
391                    model.layers[il].ffn_gate_inp,
392                    model.layers[il].ffn_up_exps,
393                    model.layers[il].ffn_gate_exps,
394                    model.layers[il].ffn_down_exps,
395                    model.layers[il].ffn_exp_probs_b,
396                    n_expert, n_expert_used,
397                    LLM_FFN_SILU, hparams.expert_weights_norm,
398                    hparams.expert_weights_scale,
399                    (llama_expert_gating_func_type) hparams.expert_gating_func,
400                    il);
401            cb(routed_out, "ffn_moe_out", il);
402
403            // Process shared expert on original input
404            ggml_tensor * shared_out = build_ffn(cur,
405                    model.layers[il].ffn_up_shexp,   NULL, NULL,
406                    model.layers[il].ffn_gate_shexp, NULL, NULL,
407                    model.layers[il].ffn_down_shexp, NULL, NULL,
408                    NULL,
409                    LLM_FFN_SILU, LLM_FFN_PAR, il);
410            cb(shared_out, "ffn_shexp_out", il);
411
412            // Final output: routed_output + shared_output
413            cur = ggml_add(ctx0, routed_out, shared_out);
414            cb(cur, "ffn_out", il);
415        }
416        cur = ggml_add(ctx0, cur, ffn_inp);
417
418        cur = build_cvec(cur, il);
419        cb(cur, "l_out", il);
420
421        // input for next layer
422        inpL = cur;
423    }
424    cur = inpL;
425    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
426
427    cb(cur, "h_nextn", -1);
428    res->t_h_nextn = cur;
429
430    if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
431        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
432    }
433
434    cb(cur, "result_norm", -1);
435    res->t_embd = cur;
436
437    // lm_head
438    cur = build_lora_mm(model.output, cur, model.output_s);
439
440    cb(cur, "result_output", -1);
441    res->t_logits = cur;
442
443    ggml_build_forward_expand(gf, cur);
444}