Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_cogvlm::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);5 6    switch (hparams.n_layer()) {7        case 32: type = LLM_TYPE_13B; break;8        default: type = LLM_TYPE_UNKNOWN;9    }10}11 12void llama_model_cogvlm::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      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);20 21    // if output is NULL, init from the input tok embed22    if (output == NULL) {23        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);24    }25 26    for (int i = 0; i < n_layer; ++i) {27        auto & layer = layers[i];28 29        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);30        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd_head_k * n_head * 3}, 0);31        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);32 33        layer.visexp_attn_wqkv = create_tensor(tn(LLM_TENSOR_VISEXP_ATTN_QKV, "weight", i), {n_embd, n_embd_head_k * n_head * 3}, 0);34        layer.visexp_attn_wo = create_tensor(tn(LLM_TENSOR_VISEXP_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);35 36        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));37 38        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);39        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);40        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);41        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);42 43        layer.visexp_ffn_gate = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);44        layer.visexp_ffn_down = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);45        layer.visexp_ffn_up   = create_tensor(tn(LLM_TENSOR_VISEXP_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);46    }47}48 49std::unique_ptr<llm_graph_context> llama_model_cogvlm::build_arch_graph(const llm_graph_params & params) const {50    return std::make_unique<graph>(*this, params);51}52 53llama_model_cogvlm::graph::graph(const llama_model & model, const llm_graph_params & params) :54    llm_graph_context(params) {55    const int64_t n_embd_head = hparams.n_embd_head_v();56    const float   kq_scale    = 1.0f / sqrtf(float(n_embd_head));57 58    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());59    GGML_ASSERT(n_embd_head == n_rot);60 61    ggml_tensor * inpL;62    ggml_tensor * cur;63 64    inpL = build_inp_embd(model.tok_embd);65 66    ggml_tensor * inp_pos = build_inp_pos();67 68    auto * inp_attn = build_attn_inp_kv();69 70    // check ubatch to see if we have input tokens (text)71    // or an input embedding vector (image)72    bool is_text;73    if (ubatch.token) {74        is_text = true;75    } else {76        is_text = false;77    }78 79    for (int il = 0; il < n_layer; ++il) {80        // get either the text or image weight tensors81        ggml_tensor *wqkv, *wo, *wo_s;82        ggml_tensor *ffn_gate, *ffn_down, *ffn_up;83 84        if (is_text) {85            wqkv     = model.layers[il].wqkv;86            wo       = model.layers[il].wo;87            wo_s     = model.layers[il].wo_s;88            ffn_gate = model.layers[il].ffn_gate;89            ffn_down = model.layers[il].ffn_down;90            ffn_up   = model.layers[il].ffn_up;91        } else {92            wqkv     = model.layers[il].visexp_attn_wqkv;93            wo       = model.layers[il].visexp_attn_wo;94            wo_s     = nullptr;95            ffn_gate = model.layers[il].visexp_ffn_gate;96            ffn_down = model.layers[il].visexp_ffn_down;97            ffn_up   = model.layers[il].visexp_ffn_up;98        }99 100        ggml_tensor * inpSA = inpL;101        cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);102 103        // build self attention104        {105            ggml_tensor * qkv = build_lora_mm(wqkv, cur);106 107            // split qkv into Q, K, V along the first dimension108            ggml_tensor * Qcur =109                ggml_view_3d(ctx0, qkv, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), qkv->nb[1], 0);110            ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),111                                              qkv->nb[1], n_embd * ggml_element_size(qkv));112            ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),113                                              qkv->nb[1], 2 * n_embd * ggml_element_size(qkv));114 115            Qcur = ggml_rope(ctx0, Qcur, inp_pos, n_embd_head, rope_type);116            Kcur = ggml_rope(ctx0, Kcur, inp_pos, n_embd_head, rope_type);117 118            cur = build_attn(inp_attn,119                wo, nullptr, wo_s,120                Qcur, Kcur, Vcur,121                nullptr, nullptr, nullptr,122                kq_scale, il);123            cb(cur, "attn_out", il);124        }125 126        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);127        cb(ffn_inp, "ffn_inp", il);128 129        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);130        cb(cur, "ffn_norm", il);131 132        cur = build_ffn(cur,133                ffn_up, NULL, NULL,134                ffn_gate, NULL, NULL,135                ffn_down, NULL, NULL,136                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);137 138        cur = ggml_add(ctx0, cur, ffn_inp);139        cb(cur, "ffn_out", il);140 141        cur = build_cvec(cur, il);142        cb(cur, "l_out", il);143 144        // input for next layer145        inpL = cur;146    }147 148    cur = inpL;149 150    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);151    cb(cur, "result_norm", -1);152    res->t_embd = cur;153 154    cur = build_lora_mm(model.output, cur, model.output_s);155    cb(cur, "result_output", -1);156    res->t_logits = cur;157    ggml_build_forward_expand(gf, cur);158}159