Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_dream::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    // Dream models are primarily 7B with 28 layers7    switch (hparams.n_layer()) {8        case 28:9            type = LLM_TYPE_7B;10            break;11        default:12            type = LLM_TYPE_UNKNOWN;13    }14    // Set non-causal attention for diffusion models15    hparams.causal_attn = false;16}17 18void llama_model_dream::load_arch_tensors(llama_model_loader &) {19    LLAMA_LOAD_LOCALS;20 21    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);22 23    // output24    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);25    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);26    output_b    = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, TENSOR_NOT_REQUIRED);27    // if output is NULL, init from the input tok embed28    if (output == NULL) {29        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);30    }31 32    for (int i = 0; i < n_layer; ++i) {33        auto & layer = layers[i];34 35        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);36 37        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);38        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);39 40        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);41 42        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);43        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);44        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);45    }46}47 48std::unique_ptr<llm_graph_context> llama_model_dream::build_arch_graph(const llm_graph_params & params) const {49    return std::make_unique<graph>(*this, params);50}51 52llama_model_dream::graph::graph(const llama_model & model, const llm_graph_params & params) :53    llm_graph_context(params) {54    //copied from qwen255    const int64_t n_embd_head = hparams.n_embd_head_v();56 57    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());58    GGML_ASSERT(n_embd_head == n_rot);59 60    ggml_tensor * cur;61    ggml_tensor * inpL;62 63    inpL = build_inp_embd(model.tok_embd);64 65    // inp_pos - contains the positions66    ggml_tensor * inp_pos = build_inp_pos();67 68    auto * inp_attn = build_attn_inp_no_cache();69 70    ggml_tensor * inp_out_ids = build_inp_out_ids();71 72    for (int il = 0; il < n_layer; ++il) {73        ggml_tensor * inpSA = inpL;74 75        // norm76        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);77        cb(cur, "attn_norm", il);78 79        // self-attention80        {81            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,82                    n_embd_head, n_head, n_head_kv, il);83 84            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,85                                 ext_factor, attn_factor, beta_fast, beta_slow);86 87            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,88                                 ext_factor, attn_factor, beta_fast, beta_slow);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, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);107        cb(cur, "ffn_norm", il);108 109        cur = build_ffn(cur,110            model.layers[il].ffn_up, NULL, NULL,111            model.layers[il].ffn_gate, NULL, NULL,112            model.layers[il].ffn_down, NULL, NULL,113            NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);114        cb(cur, "ffn_out", il);115 116        cur = ggml_add(ctx0, cur, ffn_inp);117 118        cur = build_cvec(cur, il);119        cb(cur, "l_out", il);120 121        // input for next layer122        inpL = cur;123    }124    cur = inpL;125 126    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);127 128    cb(cur, "result_norm", -1);129    res->t_embd = cur;130 131    // lm_head132    cur = build_lora_mm(model.output, cur, model.output_s);133 134    cb(cur, "result_output", -1);135    res->t_logits = cur;136 137    ggml_build_forward_expand(gf, cur);138}139