Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_codeshell::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 42: type = LLM_TYPE_7B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10}11 12void llama_model_codeshell::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}, TENSOR_NOT_REQUIRED);16 17    // if tok embd is NULL, init from output18    if (tok_embd == NULL) {19        tok_embd = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);20    }21 22    // output23    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);24    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);25    output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);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        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);32 33        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);34 35        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);36        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);37 38        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);39        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);40 41        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);42        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);43 44        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);45        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);46    }47}48 49std::unique_ptr<llm_graph_context> llama_model_codeshell::build_arch_graph(const llm_graph_params & params) const {50    return std::make_unique<graph>(*this, params);51}52 53llama_model_codeshell::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {54    const int64_t n_embd_head = hparams.n_embd_head_v();55 56    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());57    GGML_ASSERT(n_embd_head == n_rot);58 59    ggml_tensor * cur;60    ggml_tensor * inpL;61 62    inpL = build_inp_embd(model.tok_embd);63 64    // inp_pos - contains the positions65    ggml_tensor * inp_pos = build_inp_pos();66 67    auto * inp_attn = build_attn_inp_kv();68 69    ggml_tensor * inp_out_ids = build_inp_out_ids();70 71    for (int il = 0; il < n_layer; ++il) {72        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            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,81                    n_embd_head, n_head, n_head_kv, il);82 83            Qcur = ggml_rope_ext(84                    ctx0, Qcur, inp_pos, nullptr,85                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,86                    ext_factor, attn_factor, beta_fast, beta_slow87                    );88 89            Kcur = ggml_rope_ext(90                    ctx0, Kcur, inp_pos, nullptr,91                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,92                    ext_factor, attn_factor, beta_fast, beta_slow93                    );94 95            cb(Qcur, "Qcur", il);96            cb(Kcur, "Kcur", il);97            cb(Vcur, "Vcur", il);98 99            cur = build_attn(inp_attn,100                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,101                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);102        }103 104        if (il == n_layer - 1 && inp_out_ids) {105            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);106            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);107        }108 109        // add the input110        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);111        cb(ffn_inp, "ffn_inp", il);112 113        // FF114        {115            cur = build_norm(ffn_inp,116                    model.layers[il].ffn_norm,117                    model.layers[il].ffn_norm_b,118                    LLM_NORM, il);119            cb(cur, "ffn_norm", il);120 121            cur = build_ffn(cur,122                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,123                    NULL,                      NULL,                        NULL,124                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,125                    NULL,126                    LLM_FFN_GELU, LLM_FFN_SEQ, il);127            cb(cur, "ffn_out", il);128        }129 130        cur = ggml_add(ctx0, cur, ffn_inp);131 132        cur = build_cvec(cur, il);133        cb(cur, "l_out", il);134 135        // input for next layer136        inpL = cur;137    }138 139    cur = build_norm(inpL,140            model.output_norm,141            model.output_norm_b,142            LLM_NORM, -1);143 144    cb(cur, "result_norm", -1);145    res->t_embd = cur;146 147    cur = build_lora_mm(model.output, cur, model.output_s);148 149    cb(cur, "result_output", -1);150    res->t_logits = cur;151 152    ggml_build_forward_expand(gf, cur);153}154