Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_glm4::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);5    ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);6 7    switch (hparams.n_layer()) {8        case 17: type = LLM_TYPE_1B; break; // GLM-OCR9        case 40: type = LLM_TYPE_9B; break;10        case 61: type = LLM_TYPE_32B; break;11        default: type = LLM_TYPE_UNKNOWN;12    }13}14 15void llama_model_glm4::load_arch_tensors(llama_model_loader &) {16    LLAMA_LOAD_LOCALS;17 18    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);19 20    // output21    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);22    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);23    // if output is NULL, init from the input tok embed24    if (output == NULL) {25        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);26    }27 28    for (int i = 0; i < n_layer_all; ++i) {29        int flags = 0;30        if (i >= n_layer) {31            // skip all tensors in the NextN layers32            flags |= TENSOR_SKIP;33        }34 35        auto & layer = layers[i];36 37        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);38        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);39 40        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, flags);41 42        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, flags);43 44        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);45        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);46        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, flags);47 48        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, flags);49 50        // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers51        if (i >= n_layer) {52            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);53            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);54            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);55 56            // Optional tensors57            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);58            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);59            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);60        }61    }62}63 64std::unique_ptr<llm_graph_context> llama_model_glm4::build_arch_graph(const llm_graph_params & params) const {65    return std::make_unique<graph>(*this, params);66}67 68llama_model_glm4::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {69    const int64_t n_embd_head = hparams.n_embd_head_v();70 71    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());72 73    int sections[4];74    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);75 76    ggml_tensor * cur;77    ggml_tensor * inpL;78 79    inpL = build_inp_embd(model.tok_embd);80 81    bool use_mrope = hparams.use_mrope();82    if (ubatch.embd && !use_mrope) {83        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results84        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");85    }86 87    // inp_pos - contains the positions88    ggml_tensor * inp_pos = build_inp_pos();89 90    auto * inp_attn = build_attn_inp_kv();91 92    ggml_tensor * inp_out_ids = build_inp_out_ids();93 94    // Only process up to last layer (skip final NextN layer)95    // Final layer tensors are loaded but not processed in forward pass96    for (int il = 0; il < n_layer; ++il) {97        ggml_tensor * inpSA = inpL;98 99        // Pre-attention norm100        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);101        cb(cur, "attn_norm", il);102 103        // self-attention104        {105            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,106                    n_embd_head, n_head, n_head_kv, il);107 108            if (use_mrope) {109                Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,110                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,111                            ext_factor, attn_factor, beta_fast, beta_slow);112 113                Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,114                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,115                            ext_factor, attn_factor, beta_fast, beta_slow);116            } else {117                // Normal RoPE118                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,119                                    rope_type, n_ctx_orig, freq_base, freq_scale,120                                    ext_factor, attn_factor, beta_fast, beta_slow);121 122                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,123                                    rope_type, n_ctx_orig, freq_base, freq_scale,124                                    ext_factor, attn_factor, beta_fast, beta_slow);125            }126 127            cb(Qcur, "Qcur", il);128            cb(Kcur, "Kcur", il);129            cb(Vcur, "Vcur", il);130 131            cur = build_attn(inp_attn,132                    model.layers[il].wo, NULL, model.layers[il].wo_s,133                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);134        }135        if (il == n_layer - 1 && inp_out_ids) {136            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);137            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);138        }139        // Post-attention norm (new!)140        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);141        cb(cur, "post_attn_norm", il);142 143        // Add the input (residual connection after post-attention norm)144        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);145        cb(ffn_inp, "ffn_inp", il);146 147        // FF148        {149            // Pre-MLP norm150            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);151            cb(cur, "ffn_norm", il);152 153            // MLP154            cur = build_ffn(cur,155                    model.layers[il].ffn_up, NULL, NULL,156                    NULL, NULL, NULL,157                    model.layers[il].ffn_down, NULL, NULL,158                    NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);159            cb(cur, "ffn_out", il);160 161            // Post-MLP norm162            cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);163            cb(cur, "post_mlp_norm", il);164        }165        cur = ggml_add(ctx0, cur, ffn_inp);166 167        cur = build_cvec(cur, il);168        cb(cur, "l_out", il);169 170        // input for next layer171        inpL = cur;172    }173    // Final norm174    cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);175 176    cb(cur, "result_norm", -1);177    res->t_embd = cur;178 179    // Output projection180    cur = build_lora_mm(model.output, cur, model.output_s);181 182    cb(cur, "result_output", -1);183    res->t_logits = cur;184 185    ggml_build_forward_expand(gf, cur);186}187