Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_arwkv7::load_arch_hparams(llama_model_loader & ml) {4    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,                hparams.f_norm_eps, false);5    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,            hparams.f_norm_rms_eps, false);6    ml.get_key(LLM_KV_WKV_HEAD_SIZE,                          hparams.wkv_head_size);7    ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK,              hparams.n_lora_decay);8    ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK,               hparams.n_lora_iclr);9    ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);10    ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK,               hparams.n_lora_gate, false);11    ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT,                      hparams.token_shift_count, false);12 13    switch (hparams.n_layer()) {14        case 12:15            switch (hparams.n_embd) {16                case 768: type = LLM_TYPE_190M; break;17                default: type = LLM_TYPE_UNKNOWN;18            } break;19        case 24:20            switch (hparams.n_embd) {21                case 1024: type = LLM_TYPE_450M; break;22                case 2048: type = LLM_TYPE_1_5B; break;23                default: type = LLM_TYPE_UNKNOWN;24            } break;25        case 28:26            switch (hparams.n_embd) {27                case 1536: type = LLM_TYPE_1_5B; break;28                case 3584: type = LLM_TYPE_7B; break;29                default: type = LLM_TYPE_UNKNOWN;30            } break;31        case 32:32            switch (hparams.n_embd) {33                case 2560: type = LLM_TYPE_2_9B; break;34                case 4096: type = LLM_TYPE_7B; break;35                default: type = LLM_TYPE_UNKNOWN;36            } break;37        case 61:38            switch (hparams.n_embd) {39                case 4096: type = LLM_TYPE_14B; break;40                default: type = LLM_TYPE_UNKNOWN;41            } break;42        default: type = LLM_TYPE_UNKNOWN;43    }44}45 46void llama_model_arwkv7::load_arch_tensors(llama_model_loader &) {47    LLAMA_LOAD_LOCALS;48 49    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);50 51    // output52    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);53    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);54 55    const int n_lora_decay = hparams.n_lora_decay;56    const int n_lora_iclr = hparams.n_lora_iclr;57    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;58    const int n_lora_gate = hparams.n_lora_gate;59    const int attn_hidden_size = n_embd;60 61    for (int i = 0; i < n_layer; ++i) {62        auto & layer = layers[i];63 64        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);65 66        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);67        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);68        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);69 70        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);71        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);72        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);73 74        if (i == 0) {75            // actually not used76            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);77            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);78            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);79        } else {80            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);81            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);82            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);83        }84 85        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, TENSOR_NOT_REQUIRED);86        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, TENSOR_NOT_REQUIRED);87 88        try {89            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);90        } catch(std::runtime_error & e) {91            // ARWKV models may not have gate tensors92            layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);93        }94 95        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);96        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);97        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);98 99        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);100        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);101        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);102 103        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);104        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);105        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);106 107        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);108 109        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);110        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);111        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);112    }113 114}115 116std::unique_ptr<llm_graph_context> llama_model_arwkv7::build_arch_graph(const llm_graph_params & params) const {117    return std::make_unique<graph>(*this, params);118}119 120llama_model_arwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {121    GGML_ASSERT(n_embd == hparams.n_embd_r());122 123    ggml_tensor * cur;124    ggml_tensor * inpL;125    ggml_tensor * v_first = nullptr;126 127    inpL = build_inp_embd(model.tok_embd);128 129    auto * rs_inp = build_rs_inp();130 131    const auto n_embd = hparams.n_embd;132    const auto n_seq_tokens = ubatch.n_seq_tokens;133    const auto n_seqs = ubatch.n_seqs;134 135    ggml_tensor * inp_out_ids = build_inp_out_ids();136 137    for (int il = 0; il < n_layer; ++il) {138        const llama_layer * layer = &model.layers[il];139        inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);140 141        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);142 143        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);144        cb(att_norm, "attn_norm", il);145 146        ggml_tensor * x_prev = ggml_concat(147                ctx0,148                token_shift,149                ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),150                1151                );152 153        cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);154 155        token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));156        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));157 158        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);159        cb(ffn_inp, "ffn_inp", il);160 161        cur     = ggml_reshape_2d(ctx0, cur,     n_embd, n_tokens);162        ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);163 164        if (il == n_layer - 1 && inp_out_ids) {165            cur     = ggml_get_rows(ctx0, cur,     inp_out_ids);166            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);167        }168        // feed-forward network169        cur = build_norm(ffn_inp,170                model.layers[il].ffn_norm, NULL,171                LLM_NORM_RMS, il);172        cb(cur, "ffn_norm", il);173 174        cur = build_ffn(cur,175                model.layers[il].ffn_up,   NULL, NULL,176                model.layers[il].ffn_gate, NULL, NULL,177                model.layers[il].ffn_down, NULL, NULL,178                NULL,179                LLM_FFN_SILU, LLM_FFN_PAR, il);180        cb(cur, "ffn_out", il);181 182        cur = ggml_add(ctx0, cur, ffn_inp);183 184        cur = build_cvec(cur, il);185        cb(cur, "l_out", il);186 187        // input for next layer188        inpL = cur;189    }190    cur = inpL;191    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);192 193    cb(cur, "result_norm", -1);194    res->t_embd = cur;195 196    cur = build_lora_mm(model.output, cur, model.output_s);197 198    cb(cur, "result_output", -1);199    res->t_logits = cur;200 201    ggml_build_forward_expand(gf, cur);202}203