Felipe97/llama-cpp-compiled

1#include "models.h"2#include <float.h>3 4void llama_model_chameleon::load_arch_hparams(llama_model_loader & ml) {5    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);6    hparams.f_norm_eps = 1e-5;  // eps for qk-norm, torch default7    ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm, false);8 9    switch (hparams.n_layer()) {10        case 32: type = LLM_TYPE_7B; break;11        case 48: type = LLM_TYPE_34B; break;12        default: type = LLM_TYPE_UNKNOWN;13   }14}15 16void llama_model_chameleon::load_arch_tensors(llama_model_loader &) {17    LLAMA_LOAD_LOCALS;18 19    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);20 21    // output22    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);23    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);24    // if output is NULL, init from the input tok embed25    if (output == NULL) {26        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);27    }28 29    for (int i = 0; i < n_layer; ++i) {30        auto & layer = layers[i];31 32        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);33        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);34        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);35        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, TENSOR_NOT_REQUIRED);36        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);37 38        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, 0);39        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);40 41        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);42 43        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);44        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);45        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);46    }47}48 49std::unique_ptr<llm_graph_context> llama_model_chameleon::build_arch_graph(const llm_graph_params & params) const {50    return std::make_unique<graph>(*this, params);51}52 53llama_model_chameleon::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        ggml_tensor * inpSA = inpL;73 74        // norm75        if (hparams.swin_norm) {76            cur = inpL;77        } else {78            cur = build_norm(inpL,79                    model.layers[il].attn_norm, NULL,80                    LLM_NORM_RMS, il);81            cb(cur, "attn_norm", il);82        }83 84        // self-attention85        {86            // compute Q and K and RoPE them87            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,88                    n_embd_head, n_head, n_head_kv, il);89 90            if (model.layers[il].attn_q_norm) {91                Qcur = build_norm(Qcur,92                        model.layers[il].attn_q_norm,93                        model.layers[il].attn_q_norm_b,94                        LLM_NORM, il);95                cb(Qcur, "Qcur", il);96            }97 98            if (model.layers[il].attn_k_norm) {99                Kcur = build_norm(Kcur,100                        model.layers[il].attn_k_norm,101                        model.layers[il].attn_k_norm_b,102                        LLM_NORM, il);103                cb(Kcur, "Kcur", il);104            }105 106            Qcur = ggml_rope_ext(107                    ctx0, Qcur, inp_pos, nullptr,108                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,109                    ext_factor, attn_factor, beta_fast, beta_slow110                    );111 112            Kcur = ggml_rope_ext(113                    ctx0, Kcur, inp_pos, nullptr,114                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,115                    ext_factor, attn_factor, beta_fast, beta_slow116                    );117 118            cb(Qcur, "Qcur", il);119            cb(Kcur, "Kcur", il);120            cb(Vcur, "Vcur", il);121 122            cur = build_attn(inp_attn,123                    model.layers[il].wo, nullptr, model.layers[il].wo_s,124                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);125        }126 127        if (il == n_layer - 1 && inp_out_ids) {128            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);129            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);130        }131 132        if (hparams.swin_norm) {133            cur = build_norm(cur,134                    model.layers[il].attn_norm, NULL,135                    LLM_NORM_RMS, il);136        }137 138        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);139        cb(ffn_inp, "ffn_inp", il);140 141        // feed-forward network142        if (!hparams.swin_norm) {143            cur = build_norm(ffn_inp,144                    model.layers[il].ffn_norm, NULL,145                    LLM_NORM_RMS, il);146            cb(cur, "ffn_norm", il);147        }148 149        cur = build_ffn(cur,150                model.layers[il].ffn_up,   NULL, NULL,151                model.layers[il].ffn_gate, NULL, NULL,152                model.layers[il].ffn_down, NULL, NULL,153                NULL,154                LLM_FFN_SILU, LLM_FFN_PAR, il);155        cb(cur, "ffn_out", il);156 157        if (hparams.swin_norm) {158            cur = build_norm(cur,159                    model.layers[il].ffn_norm, NULL,160                    LLM_NORM_RMS, il);161            cb(cur, "ffn_norm", il);162        }163 164        cur = ggml_add(ctx0, cur, ffn_inp);165        cb(cur, "ffn_out", il);166 167        cur = build_cvec(cur, il);168        cb(cur, "l_out", il);169 170        // input for next layer171        inpL = cur;172    }173 174    cur = inpL;175 176    cur = build_norm(cur,177            model.output_norm, NULL,178            LLM_NORM_RMS, -1);179 180    cb(cur, "result_norm", -1);181    res->t_embd = cur;182 183    // lm_head184    cur = build_lora_mm(model.output, cur, model.output_s);185    cb(cur, "result_output_with_img_logits", -1);186 187    // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.188    // Needs to be removed once image outputs are supported.189    int img_token_end_idx = 8196;190    int img_token_start_idx = 4;191    int num_img_tokens = img_token_end_idx - img_token_start_idx;192    // creates 1d tensor of size num_img_tokens and values -FLT_MAX,193    // which ensures that text token values are always at least larger than image token values194    ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens);195    img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX);196    cb(img_logits, "img_logits", -1);197 198    cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx);199 200    cb(cur, "result_output", -1);201    res->t_logits = cur;202 203    ggml_build_forward_expand(gf, cur);204}205