Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_qwen3vl::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);5    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);6    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);7 8    switch (hparams.n_layer()) {9        case 28: type = LLM_TYPE_1_7B; break;10        case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;11        case 64: type = LLM_TYPE_32B; break;12        default: type = LLM_TYPE_UNKNOWN;13    }14}15 16void llama_model_qwen3vl::load_arch_tensors(llama_model_loader &) {17    LLAMA_LOAD_LOCALS;18 19    int64_t n_vocab_out = n_vocab;20    if (arch == LLM_ARCH_QWEN3TTS) {21        // [TAG_LLAMA_N_VOCAB_OUT]22        n_vocab_out = 3072;23    }24 25    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);26 27    // output28    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);29    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab_out}, TENSOR_NOT_REQUIRED);30    // if output is NULL, init from the input tok embed31    if (output == NULL) {32        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);33    }34 35    // output rerank head36    cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);37 38    for (int i = 0; i < n_layer; ++i) {39        auto & layer = layers[i];40 41        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);42 43        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);44        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);45 46        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);47        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);48 49        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);50        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);51        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);52        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);53    }54}55 56std::unique_ptr<llm_graph_context> llama_model_qwen3vl::build_arch_graph(const llm_graph_params & params) const {57    return std::make_unique<graph>(*this, params);58}59 60llama_model_qwen3vl::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {61    const size_t n_deepstack_layers = hparams.n_deepstack_layers;62 63    const int64_t n_embd      = hparams.n_embd;64    const int64_t n_embd_head = hparams.n_embd_head_v();65 66    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());67    GGML_ASSERT(n_embd_head == n_rot);68 69    ggml_tensor * cur;70    ggml_tensor * inpL;71 72    inpL = build_inp_embd(model.tok_embd);73 74    int sections[4];75    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);76 77    // inp_pos - contains the positions78    ggml_tensor * inp_pos = build_inp_pos();79 80    auto * inp_attn = build_attn_inp_kv();81 82    ggml_tensor * inp_out_ids = build_inp_out_ids();83 84    for (int il = 0; il < n_layer; ++il) {85        ggml_tensor * inpSA = inpL;86 87        // norm88        cur = build_norm(inpL,89                model.layers[il].attn_norm, NULL,90                LLM_NORM_RMS, il);91        cb(cur, "attn_norm", il);92 93        // self-attention94        {95            // compute Q and K and RoPE them96            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,97                    n_embd_head, n_head, n_head_kv, il);98 99            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);100            cb(Qcur, "Qcur_normed", il);101 102            Qcur = ggml_rope_multi(103                    ctx0, Qcur, inp_pos, nullptr,104                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,105                    ext_factor, attn_factor, beta_fast, beta_slow106                    );107 108            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);109            cb(Kcur, "Kcur_normed", il);110 111            Kcur = ggml_rope_multi(112                    ctx0, Kcur, inp_pos, nullptr,113                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,114                    ext_factor, attn_factor, beta_fast, beta_slow115                    );116 117            cb(Qcur, "Qcur", il);118            cb(Kcur, "Kcur", il);119            cb(Vcur, "Vcur", il);120 121            cur = build_attn(inp_attn,122                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,123                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);124        }125 126        if (il == n_layer - 1 && inp_out_ids) {127            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);128            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);129        }130 131        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);132        cb(ffn_inp, "ffn_inp", il);133 134        // feed-forward network135        cur = build_norm(ffn_inp,136                model.layers[il].ffn_norm, NULL,137                LLM_NORM_RMS, il);138        cb(cur, "ffn_norm", il);139 140        cur = build_ffn(cur,141                model.layers[il].ffn_up,   NULL, NULL,142                model.layers[il].ffn_gate, NULL, NULL,143                model.layers[il].ffn_down, NULL, NULL,144                NULL,145                LLM_FFN_SILU, LLM_FFN_PAR, il);146        cb(cur, "ffn_out", il);147 148        cur = ggml_add(ctx0, cur, ffn_inp);149 150        cur = build_cvec(cur, il);151        cb(cur, "l_out", il);152 153        if (il < (int) n_deepstack_layers) {154            ggml_tensor * ds = ggml_view_2d(ctx0, res->t_inp_embd, n_embd, n_tokens, res->t_inp_embd->nb[1], (il + 1) * n_embd * sizeof(float));155            cur = ggml_add(ctx0, cur, ds);156            cb(cur, "deepstack_out", il);157        }158 159        // input for next layer160        inpL = cur;161    }162 163    cur = inpL;164 165    cur = build_norm(cur,166            model.output_norm, NULL,167            LLM_NORM_RMS, -1);168 169    cb(cur, "result_norm", -1);170    res->t_embd = cur;171 172    // lm_head173    cur = build_lora_mm(model.output, cur, model.output_s);174 175    int64_t n_vocab_in  = model.tok_embd->ne[1];176    int64_t n_vocab_out = model.output->ne[1];177    if (n_vocab_in > n_vocab_out) {178        // case: Qwen3TTS model with codec_head as output179        GGML_ASSERT(model.output_norm);180        int64_t pad = n_vocab_in - n_vocab_out;181 182        // using this trick to get a scalar -inf tensor to pad the output183        ggml_tensor * neg_inf = ggml_scale_bias(ctx0,184                ggml_view_1d(ctx0, model.output_norm, 1, 0),185                0.0f, -INFINITY);186        neg_inf = ggml_repeat_4d(ctx0, neg_inf, pad, cur->ne[1], 1, 1);187        cur = ggml_concat(ctx0, neg_inf, cur, 0); // [padded .. n_vocab_out, n_stream]188 189    } else if (n_vocab_in < n_vocab_out) {190        GGML_ABORT("invalid case");191    }192 193    cb(cur, "result_output", -1);194    res->t_logits = cur;195 196    ggml_build_forward_expand(gf, cur);197}198