Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_hunyuan_vl::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);6 7    // XDRoPE / NTK-aware scaling: base = rope_theta * alpha^(dim / (dim - 2))8    if (hparams.rope_scaling_alpha > 0.0f) {9        const int dim = hparams.n_embd_head_k();10        hparams.rope_freq_base_train = hparams.rope_freq_base_train11            * powf(hparams.rope_scaling_alpha, (float)dim / (float)(dim - 2));12    }13 14    switch (hparams.n_embd) {15        case 1024: type = LLM_TYPE_0_5B; break;16        case 2048: type = LLM_TYPE_1_8B; break;17        case 3072: type = LLM_TYPE_4B; break;18        case 4096: type = LLM_TYPE_7B; break;19        default: type = LLM_TYPE_UNKNOWN;20    }21}22 23void llama_model_hunyuan_vl::load_arch_tensors(llama_model_loader &) {24    LLAMA_LOAD_LOCALS;25 26    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);27 28    // output29    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);30    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);31    // if output is NULL, init from the input tok embed32    if (output == NULL) {33        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);34    }35 36    for (int i = 0; i < n_layer; ++i) {37        auto & layer = layers[i];38 39        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);40 41        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);42        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);43 44        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);45        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);46 47        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);48 49        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);50        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);51        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);52 53    }54}55 56std::unique_ptr<llm_graph_context> llama_model_hunyuan_vl::build_arch_graph(const llm_graph_params & params) const {57    return std::make_unique<graph>(*this, params);58}59 60llama_model_hunyuan_vl::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {61    const int64_t n_embd_head = hparams.n_embd_head_v();62 63    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());64    GGML_ASSERT(n_embd_head == n_rot);65 66    const bool use_mrope = hparams.use_mrope();67 68    int sections[4];69    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);70 71    ggml_tensor * cur;72    ggml_tensor * inpL;73 74    inpL = build_inp_embd(model.tok_embd);75 76    // inp_pos - contains the positions77    ggml_tensor * inp_pos = build_inp_pos();78 79    auto * inp_attn = build_attn_inp_kv();80 81    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));82 83    ggml_tensor * inp_out_ids = build_inp_out_ids();84 85    for (int il = 0; il < n_layer; ++il) {86        ggml_tensor * inpSA = inpL;87 88        // norm89        cur = build_norm(inpL,90                model.layers[il].attn_norm, NULL,91                LLM_NORM_RMS, il);92        cb(cur, "attn_norm", il);93        // self-attention94        {95            // rope freq factors for llama3; may return nullptr for llama2 and other models96            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);97 98            // compute Q and K and RoPE them99            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,100                    n_embd_head, n_head, n_head_kv, il);101 102            if (use_mrope) {103                Qcur = ggml_rope_multi(104                            ctx0, Qcur, inp_pos, rope_factors,105                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,106                            ext_factor, attn_factor, beta_fast, beta_slow107                            );108 109                Kcur = ggml_rope_multi(110                            ctx0, Kcur, inp_pos, rope_factors,111                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,112                            ext_factor, attn_factor, beta_fast, beta_slow113                            );114            } else {115                Qcur = ggml_rope_ext(116                            ctx0, Qcur, inp_pos, rope_factors,117                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,118                            ext_factor, attn_factor, beta_fast, beta_slow119                            );120 121                Kcur = ggml_rope_ext(122                            ctx0, Kcur, inp_pos, rope_factors,123                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,124                            ext_factor, attn_factor, beta_fast, beta_slow125                            );126            }127 128            cb(Qcur, "Qcur", il);129            cb(Kcur, "Kcur", il);130            cb(Vcur, "Vcur", il);131 132            Kcur = build_norm(Kcur,133                        model.layers[il].attn_k_norm, nullptr,134                        LLM_NORM_RMS, il);135            cb(Kcur, "Kcur_norm", il);136 137            Qcur = build_norm(Qcur,138                        model.layers[il].attn_q_norm, nullptr,139                        LLM_NORM_RMS, il);140            cb(Qcur, "Qcur_norm", il);141 142            cur = build_attn(inp_attn,143                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,144                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);145            cb(cur, "attn_out", il);146        }147        if (il == n_layer - 1 && inp_out_ids) {148            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);149            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);150        }151        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);152        cb(ffn_inp, "ffn_inp", il);153 154        cur = build_norm(ffn_inp,155                model.layers[il].ffn_norm, NULL,156                LLM_NORM_RMS, il);157        cb(cur, "ffn_norm", il);158        // feed-forward network (non-MoE)159        ggml_tensor * cur_mlp = build_ffn(cur,160                    model.layers[il].ffn_up,   NULL, NULL,161                    model.layers[il].ffn_gate, NULL, NULL,162                    model.layers[il].ffn_down, NULL, NULL,163                    NULL,164                    LLM_FFN_SILU, LLM_FFN_PAR, il);165        cb(cur_mlp, "ffn_out", il);166 167        cur = ggml_add(ctx0, cur_mlp, ffn_inp);168 169        cur = build_cvec(cur, il);170        cb(cur, "l_out", il);171 172        // input for next layer173        inpL = cur;174    }175    cur = inpL;176 177    cur = build_norm(cur,178            model.output_norm, NULL,179            LLM_NORM_RMS, -1);180 181    cb(cur, "result_norm", -1);182    res->t_embd = cur;183    // lm_head184    cur = build_lora_mm(model.output, cur, model.output_s);185    cb(cur, "result_output", -1);186    res->t_logits = cur;187 188    ggml_build_forward_expand(gf, cur);189}190