Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_qwen2vl::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);5}6// fall through7 8void llama_model_qwen2vl::load_arch_tensors(llama_model_loader &) {9    LLAMA_LOAD_LOCALS;10 11    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);12 13    // output14    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);15    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);16    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);17    // if output is NULL, init from the input tok embed18    if (output == NULL) {19        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);20    }21 22    for (int i = 0; i < n_layer; ++i) {23        auto & layer = layers[i];24 25        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);26 27        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);28        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);29 30        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);31 32        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);33        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);34        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);35    }36}37 38std::unique_ptr<llm_graph_context> llama_model_qwen2vl::build_arch_graph(const llm_graph_params & params) const {39    return std::make_unique<graph>(*this, params);40}41 42llama_model_qwen2vl::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {43    const int64_t n_embd_head = hparams.n_embd_head_v();44 45    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());46    GGML_ASSERT(n_embd_head == n_rot);47 48    ggml_tensor * cur;49    ggml_tensor * inpL;50 51    inpL = build_inp_embd(model.tok_embd);52 53    // inp_pos - contains the positions54    ggml_tensor * inp_pos = build_inp_pos();55 56    auto * inp_attn = build_attn_inp_kv();57 58    int sections[4];59    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);60 61    ggml_tensor * inp_out_ids = build_inp_out_ids();62 63    for (int il = 0; il < n_layer; ++il) {64        ggml_tensor * inpSA = inpL;65 66        // norm67        cur = build_norm(inpL,68                model.layers[il].attn_norm, NULL,69                LLM_NORM_RMS, il);70        cb(cur, "attn_norm", il);71 72        // self-attention73        {74            // compute Q and K and RoPE them75            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,76                    n_embd_head, n_head, n_head_kv, il);77 78            Qcur = ggml_rope_multi(79                    ctx0, Qcur, inp_pos, nullptr,80                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,81                    ext_factor, attn_factor, beta_fast, beta_slow82                    );83 84            Kcur = ggml_rope_multi(85                    ctx0, Kcur, inp_pos, nullptr,86                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,87                    ext_factor, attn_factor, beta_fast, beta_slow88                    );89 90            cb(Qcur, "Qcur", il);91            cb(Kcur, "Kcur", il);92            cb(Vcur, "Vcur", il);93 94            cur = build_attn(inp_attn,95                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,96                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);97        }98        if (il == n_layer - 1 && inp_out_ids) {99            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);100            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);101        }102        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);103        cb(ffn_inp, "ffn_inp", il);104 105        // feed-forward network106        cur = build_norm(ffn_inp,107                model.layers[il].ffn_norm, NULL,108                LLM_NORM_RMS, il);109        cb(cur, "ffn_norm", il);110 111        cur = build_ffn(cur,112                model.layers[il].ffn_up,   NULL, NULL,113                model.layers[il].ffn_gate, NULL, NULL,114                model.layers[il].ffn_down, NULL, NULL,115                NULL,116                LLM_FFN_SILU, LLM_FFN_PAR, il);117        cb(cur, "ffn_out", il);118 119        cur = ggml_add(ctx0, cur, ffn_inp);120 121        cur = build_cvec(cur, il);122        cb(cur, "l_out", il);123 124        // input for next layer125        inpL = cur;126    }127    cur = inpL;128 129    cur = build_norm(cur,130            model.output_norm, NULL,131            LLM_NORM_RMS, -1);132 133    cb(cur, "result_norm", -1);134    res->t_embd = cur;135 136    // lm_head137    cur = build_lora_mm(model.output, cur, model.output_s);138 139    cb(cur, "result_output", -1);140    res->t_logits = cur;141 142    ggml_build_forward_expand(gf, cur);143}144