Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_arcee::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    // Arcee uses the same structure as Llama7    switch (hparams.n_layer()) {8        case 36: type = LLM_TYPE_4B; break;9        default: type = LLM_TYPE_UNKNOWN;10    }11}12 13void llama_model_arcee::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 37        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));38 39        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);40        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);41    }42}43 44std::unique_ptr<llm_graph_context> llama_model_arcee::build_arch_graph(const llm_graph_params & params) const {45    return std::make_unique<graph>(*this, params);46}47 48llama_model_arcee::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {49    const int64_t n_embd_head = hparams.n_embd_head_v();50 51    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());52    GGML_ASSERT(n_embd_head == n_rot);53 54    ggml_tensor * cur;55    ggml_tensor * inpL;56 57    inpL = build_inp_embd(model.tok_embd);58 59    // inp_pos - contains the positions60    ggml_tensor * inp_pos = build_inp_pos();61 62    auto * inp_attn = build_attn_inp_kv();63 64    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;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, NULL,74                LLM_NORM_RMS, il);75        cb(cur, "attn_norm", il);76 77        // self-attention78        {79            // rope freq factors for llama3; may return nullptr for llama2 and other models80            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);81 82            // compute Q and K and RoPE them83            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,84                    n_embd_head, n_head, n_head_kv, il);85 86            Qcur = ggml_rope_ext(87                    ctx0, Qcur, inp_pos, rope_factors,88                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,89                    ext_factor, attn_factor, beta_fast, beta_slow90                    );91 92            Kcur = ggml_rope_ext(93                    ctx0, Kcur, inp_pos, rope_factors,94                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,95                    ext_factor, attn_factor, beta_fast, beta_slow96                    );97 98            cb(Qcur, "Qcur", il);99            cb(Kcur, "Kcur", il);100            cb(Vcur, "Vcur", il);101 102            cur = build_attn(inp_attn,103                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,104                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);105            cb(cur, "attn_out", il);106        }107 108        if (il == n_layer - 1 && inp_out_ids) {109            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);110            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);111        }112 113        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);114        cb(ffn_inp, "ffn_inp", il);115 116        // feed-forward network117        // ARCEE uses relu^2 instead of silu118        cur = build_norm(ffn_inp,119                model.layers[il].ffn_norm, NULL,120                LLM_NORM_RMS, il);121        cb(cur, "ffn_norm", il);122 123        cur = build_ffn(cur,124                model.layers[il].ffn_up,   NULL, NULL,125                NULL,                      NULL, NULL,126                model.layers[il].ffn_down, NULL, NULL,127                NULL,128                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);129        cb(cur, "ffn_out", il);130 131        cur = ggml_add(ctx0, cur, ffn_inp);132        cb(cur, "ffn_out", il);133 134        cur = build_cvec(cur, il);135        cb(cur, "l_out", il);136 137        // input for next layer138        inpL = cur;139    }140 141    cur = inpL;142 143    cur = build_norm(cur,144            model.output_norm, NULL,145            LLM_NORM_RMS, -1);146 147    cb(cur, "result_norm", -1);148    res->t_embd = cur;149 150    // lm_head151    cur = build_lora_mm(model.output, cur, model.output_s);152 153    cb(cur, "result_output", -1);154    res->t_logits = cur;155 156    ggml_build_forward_expand(gf, cur);157}158