Felipe97/llama-cpp-compiled

1#include "models.h"2 3void llama_model_rwkv7::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_rwkv7::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    // Block 0, LN052    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);53    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);54 55    // output56    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);57    output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);58    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);59 60    const int n_lora_decay = hparams.n_lora_decay;61    const int n_lora_iclr = hparams.n_lora_iclr;62    const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;63    const int n_lora_gate = hparams.n_lora_gate;64    const int attn_hidden_size = n_embd;65    const int ffn_size = hparams.n_ff_arr[0];66 67    for (int i = 0; i < n_layer; ++i) {68        auto & layer = layers[i];69 70        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);71        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);72 73        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);74        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);75 76        layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);77        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);78        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);79 80        layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);81        layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);82        layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);83 84        if (i == 0) {85            // actually not used86            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);87            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);88            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);89        } else {90            layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);91            layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);92            layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);93        }94 95        layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, 0);96        layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, 0);97 98        layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);99 100        layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);101        layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);102        layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);103 104        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);105        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);106        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);107 108        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);109        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);110        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);111 112        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);113 114        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);115        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);116    }117 118}119 120std::unique_ptr<llm_graph_context> llama_model_rwkv7::build_arch_graph(const llm_graph_params & params) const {121    return std::make_unique<graph>(*this, params);122}123 124llama_model_rwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) :125    llm_build_rwkv7_base(model, params) {126    GGML_ASSERT(hparams.token_shift_count == 2);127 128    ggml_tensor * cur;129    ggml_tensor * inpL;130    ggml_tensor * v_first = nullptr;131 132    inpL = build_inp_embd(model.tok_embd);133    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, 0);134 135    auto * rs_inp = build_rs_inp();136 137    const auto n_embd       = hparams.n_embd;138    const auto n_seq_tokens = ubatch.n_seq_tokens;139    const auto n_seqs       = ubatch.n_seqs;140 141    ggml_tensor * inp_out_ids = build_inp_out_ids();142 143    for (int il = 0; il < n_layer; ++il) {144        const llama_layer * layer = &model.layers[il];145        inpL                      = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);146 147        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);148 149        ggml_tensor * att_shift =150            ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);151        ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1],152                                               token_shift->nb[2], n_embd * ggml_element_size(token_shift));153 154        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);155        cb(att_norm, "attn_norm", il);156 157        ggml_tensor * x_prev = ggml_concat(158            ctx0, att_shift,159            ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0), 1);160 161        cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);162 163        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);164        cb(ffn_inp, "ffn_inp", il);165 166        ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);167        cb(ffn_norm, "ffn_norm", il);168 169        x_prev = ggml_concat(170            ctx0, ffn_shift,171            ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0), 1);172 173        token_shift = ggml_concat(ctx0,174                                  ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2],175                                               (n_seq_tokens - 1) * n_embd * ggml_element_size(att_norm)),176                                  ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2],177                                               (n_seq_tokens - 1) * n_embd * ggml_element_size(ffn_norm)),178                                  1);179        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));180 181        ffn_inp  = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);182        ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);183        x_prev   = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens);184 185        if (il == n_layer - 1 && inp_out_ids) {186            ffn_inp  = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);187            ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);188            x_prev   = ggml_get_rows(ctx0, x_prev, inp_out_ids);189        }190        cur = build_rwkv7_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV7);191        cur = ggml_add(ctx0, cur, ffn_inp);192 193        cur = build_cvec(cur, il);194        cb(cur, "l_out", il);195 196        // input for next layer197        inpL = cur;198    }199    cur = inpL;200    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);201 202    cb(cur, "result_norm", -1);203    res->t_embd = cur;204 205    cur = build_lora_mm(model.output, cur, model.output_s);206 207    cb(cur, "result_output", -1);208    res->t_logits = cur;209 210    ggml_build_forward_expand(gf, cur);211}212