Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_smollm3::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5    hparams.n_no_rope_layer_step = 4;6 7    switch (hparams.n_layer()) {8        case 36: type = LLM_TYPE_3B; break;9        default: type = LLM_TYPE_UNKNOWN;10    }11}12 13void llama_model_smollm3::load_arch_tensors(llama_model_loader &) {14    LLAMA_LOAD_LOCALS;15 16    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);17 18    // output19    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);20    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);21 22    // if output is NULL, init from the input tok embed23    if (output == NULL) {24        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);25    }26 27    for (int i = 0; i < n_layer; ++i) {28        auto & layer = layers[i];29 30        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);31 32        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);33        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);34 35        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);36        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);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}41 42std::unique_ptr<llm_graph_context> llama_model_smollm3::build_arch_graph(const llm_graph_params & params) const {43    return std::make_unique<graph>(*this, params);44}45 46llama_model_smollm3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {47    const int64_t n_embd_head = hparams.n_embd_head_v();48 49    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());50    GGML_ASSERT(n_embd_head == n_rot);51 52    ggml_tensor * cur;53    ggml_tensor * inpL;54 55    inpL = build_inp_embd(model.tok_embd);56 57    // inp_pos - contains the positions58    ggml_tensor * inp_pos = build_inp_pos();59 60    auto * inp_attn = build_attn_inp_kv();61 62    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;63 64    ggml_tensor * inp_out_ids = build_inp_out_ids();65 66    for (int il = 0; il < n_layer; ++il) {67        ggml_tensor * inpSA = inpL;68 69        const bool use_rope = (il + 1) % hparams.n_no_rope_layer_step != 0;70 71        // norm72        cur = build_norm(inpL,73                model.layers[il].attn_norm, NULL,74                LLM_NORM_RMS, il);75        cb(cur, "attn_norm", il);76 77        // self-attention78        {79            // compute Q and K and RoPE them80            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,81                    n_embd_head, n_head, n_head_kv, il);82 83            if (use_rope) {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, kq_scale, il);103            cb(cur, "attn_out", il);104        }105        if (il == n_layer - 1 && inp_out_ids) {106            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);107            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);108        }109        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);110        cb(ffn_inp, "ffn_inp", il);111 112        // feed-forward network113        {114            cur = build_norm(ffn_inp,115                    model.layers[il].ffn_norm, NULL,116                    LLM_NORM_RMS, il);117            cb(cur, "ffn_norm", il);118 119            cur = build_ffn(cur,120                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,121                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,122                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,123                    NULL,124                    LLM_FFN_SILU, LLM_FFN_PAR, il);125            cb(cur, "ffn_out", il);126        }127        cur = ggml_add(ctx0, cur, ffn_inp);128        cb(cur, "ffn_out", il);129 130        cur = build_cvec(cur, il);131        cb(cur, "l_out", il);132 133        // input for next layer134        inpL = cur;135    }136    cur = inpL;137 138    cur = build_norm(cur,139            model.output_norm, NULL,140            LLM_NORM_RMS, -1);141 142    cb(cur, "result_norm", -1);143    res->t_embd = cur;144 145    // lm_head146    cur = build_lora_mm(model.output, cur, model.output_s);147 148    cb(cur, "result_output", -1);149    res->t_logits = cur;150 151    ggml_build_forward_expand(gf, cur);152}153