Felipe97/llama-cpp-compiled

1#include "models.h"2 3// backbone of the pocket-tts CALM pipeline: the "text" side of a flow language model.4// it has no lm_head, the audio latents are produced by the flow net inside the mmproj5 6void llama_model_pockettts::load_arch_hparams(llama_model_loader & ml) {7    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);8 9    switch (hparams.n_layer()) {10        case 6:  type = LLM_TYPE_109M; break;11        case 24: type = LLM_TYPE_335M; break;12        default: type = LLM_TYPE_UNKNOWN;13    }14}15 16void llama_model_pockettts::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    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);22    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);23    // no output head, the logits are unused; reuse the embedding table so a sampler can still run24    output        = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);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.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);31 32        create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, TENSOR_NOT_REQUIRED);33 34        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);35 36        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);37        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 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_pockettts::build_arch_graph(const llm_graph_params & params) const {45    return std::make_unique<graph>(*this, params);46}47 48llama_model_pockettts::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    ggml_tensor * inp_pos = build_inp_pos();60 61    auto * inp_attn = build_attn_inp_kv();62 63    ggml_tensor * inp_out_ids = build_inp_out_ids();64 65    for (int il = 0; il < n_layer; ++il) {66        cur = build_norm(inpL,67                model.layers[il].attn_norm,68                model.layers[il].attn_norm_b,69                LLM_NORM, il);70        cb(cur, "attn_norm", il);71 72        // self-attention73        {74            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,75                    n_embd_head, n_head, n_head_kv, il);76 77            Qcur = ggml_rope_ext(78                    ctx0, Qcur, inp_pos, nullptr,79                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,80                    ext_factor, attn_factor, beta_fast, beta_slow81                    );82 83            Kcur = ggml_rope_ext(84                    ctx0, Kcur, 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            cb(Qcur, "Qcur", il);90            cb(Kcur, "Kcur", il);91            cb(Vcur, "Vcur", il);92 93            cur = build_attn(inp_attn,94                    model.layers[il].wo, NULL, model.layers[il].wo_s,95                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);96        }97 98        if (il == n_layer - 1 && inp_out_ids) {99            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);100            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);101        }102 103        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);104        cb(ffn_inp, "ffn_inp", il);105 106        // FF107        {108            cur = build_norm(ffn_inp,109                    model.layers[il].ffn_norm,110                    model.layers[il].ffn_norm_b,111                    LLM_NORM, il);112            cb(cur, "ffn_norm", il);113 114            cur = build_ffn(cur,115                    model.layers[il].ffn_up,   NULL, NULL,116                    NULL,                      NULL, NULL,117                    model.layers[il].ffn_down, NULL, NULL,118                    NULL,119                    LLM_FFN_GELU, LLM_FFN_SEQ, il);120            cb(cur, "ffn_out", il);121        }122 123        cur = ggml_add(ctx0, cur, ffn_inp);124 125        cur = build_cvec(cur, il);126        cb(cur, "l_out", il);127 128        // input for next layer129        inpL = cur;130    }131 132    cur = build_norm(inpL,133            model.output_norm,134            model.output_norm_b,135            LLM_NORM, -1);136 137    cb(cur, "result_norm", -1);138    res->t_embd = cur;139 140    cur = build_lora_mm(model.output, cur, model.output_s);141 142    cb(cur, "result_output", -1);143    res->t_logits = cur;144 145    ggml_build_forward_expand(gf, cur);146}147