Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_nemotron::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);5 6    switch (hparams.n_layer()) {7        case 32: type = LLM_TYPE_4B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10}11 12void llama_model_nemotron::load_arch_tensors(llama_model_loader &) {13    LLAMA_LOAD_LOCALS;14 15    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);16 17    // output18    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);19    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);20    output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);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        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);27 28        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);29        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);30 31        // optional bias tensors32        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);33 34        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);35        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);36 37        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);38        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);39 40        // optional MLP bias41        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);42        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);43    }44}45 46std::unique_ptr<llm_graph_context> llama_model_nemotron::build_arch_graph(const llm_graph_params & params) const {47    return std::make_unique<graph>(*this, params);48}49 50llama_model_nemotron::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {51    const int64_t n_embd_head = hparams.n_embd_head_v();52 53    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());54    //GGML_ASSERT(n_embd_head == n_rot);55 56    ggml_tensor * cur;57    ggml_tensor * inpL;58 59    inpL = build_inp_embd(model.tok_embd);60 61    // inp_pos - contains the positions62    ggml_tensor * inp_pos = build_inp_pos();63 64    auto * inp_attn = build_attn_inp_kv();65 66    ggml_tensor * inp_out_ids = build_inp_out_ids();67 68    for (int il = 0; il < n_layer; ++il) {69        ggml_tensor * inpSA = inpL;70 71        // norm72        cur = build_norm(inpL,73                model.layers[il].attn_norm,74                model.layers[il].attn_norm_b,75                LLM_NORM, il);76        cb(cur, "attn_norm", il);77 78        // self-attention79        {80            // compute Q and K and RoPE them81            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(85                    ctx0, Qcur, inp_pos, nullptr,86                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,87                    ext_factor, attn_factor, beta_fast, beta_slow88                    );89 90            Kcur = ggml_rope_ext(91                    ctx0, Kcur, inp_pos, nullptr,92                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,93                    ext_factor, attn_factor, beta_fast, beta_slow94                    );95 96            cb(Qcur, "Qcur", il);97            cb(Kcur, "Kcur", il);98            cb(Vcur, "Vcur", il);99 100            cur = build_attn(inp_attn,101                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,102                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);103        }104        if (il == n_layer - 1 && inp_out_ids) {105            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);106            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);107        }108        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);109        cb(ffn_inp, "ffn_inp", il);110 111        // feed-forward network112        cur = build_norm(ffn_inp,113                model.layers[il].ffn_norm,114                model.layers[il].ffn_norm_b,115                LLM_NORM, il);116        cb(cur, "ffn_norm", il);117 118        cur = build_ffn(cur,119                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,120                NULL,                      NULL,                        NULL,121                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,122                NULL,123                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);124 125        cur = ggml_add(ctx0, cur, ffn_inp);126        cb(cur, "ffn_out", il);127 128        cur = build_cvec(cur, il);129        cb(cur, "l_out", il);130 131        // input for next layer132        inpL = cur;133    }134    cur = inpL;135 136    cur = build_norm(cur,137            model.output_norm, model.output_norm_b,138            LLM_NORM, -1);139 140    cb(cur, "result_norm", -1);141    res->t_embd = cur;142 143    // lm_head144    cur = build_lora_mm(model.output, cur, model.output_s);145 146    cb(cur, "result_output", -1);147    res->t_logits = cur;148 149    ggml_build_forward_expand(gf, cur);150}151