echodict/llama.cpp

1#include "models.h"2 3llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {4    const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k()));5 6    const uint32_t n_embd_head_qk_rope = hparams.n_rot();7    const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k() - hparams.n_rot();8 9    const uint32_t kv_lora_rank = hparams.n_lora_kv;10 11    ggml_tensor * cur;12    ggml_tensor * inpL;13 14    // {n_embd, n_tokens}15    inpL = build_inp_embd(model.tok_embd);16 17    // inp_pos - contains the positions18    ggml_tensor * inp_pos = build_inp_pos();19 20    auto * inp_attn = build_attn_inp_kv();21 22    ggml_tensor * inp_out_ids = build_inp_out_ids();23 24    for (int il = 0; il < n_layer; ++il) {25        ggml_tensor * inpSA = inpL;26 27        // norm28        cur = build_norm(inpL,29                model.layers[il].attn_norm, NULL,30                LLM_NORM_RMS, il);31        cb(cur, "attn_norm", il);32 33        // self_attention34        {35            ggml_tensor * q = NULL;36            q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);37            cb(q, "q", il);38 39            // split into {n_head * n_embd_head_qk_nope, n_tokens}40            ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,41                    ggml_row_size(q->type, hparams.n_embd_head_k()),42                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),43                    0);44            cb(q_nope, "q_nope", il);45 46            // and {n_head * n_embd_head_qk_rope, n_tokens}47            ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,48                    ggml_row_size(q->type, hparams.n_embd_head_k()),49                    ggml_row_size(q->type, hparams.n_embd_head_k() * n_head),50                    ggml_row_size(q->type, n_embd_head_qk_nope));51            cb(q_pe, "q_pe", il);52 53            // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}54            ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);55            cb(kv_pe_compresseed, "kv_pe_compresseed", il);56 57            // split into {kv_lora_rank, n_tokens}58            ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,59                    kv_pe_compresseed->nb[1],60                    0);61            cb(kv_compressed, "kv_compressed", il);62 63            // and {n_embd_head_qk_rope, n_tokens}64            ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,65                    kv_pe_compresseed->nb[1],66                    kv_pe_compresseed->nb[1],67                    ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));68            cb(k_pe, "k_pe", il);69 70            kv_compressed = build_norm(kv_compressed,71                    model.layers[il].attn_kv_a_norm, NULL,72                    LLM_NORM_RMS, il);73            cb(kv_compressed, "kv_compressed", il);74 75            // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}76            ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);77            cb(kv, "kv", il);78 79            // split into {n_head * n_embd_head_qk_nope, n_tokens}80            ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,81                    ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v()),82                    ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v())),83                    0);84            cb(k_nope, "k_nope", il);85 86            // and {n_head * n_embd_head_v, n_tokens}87            ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v(), n_head, n_tokens,88                    ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())),89                    ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v())*n_head),90                    ggml_row_size(kv->type, (n_embd_head_qk_nope)));91            cb(v_states, "v_states", il);92 93            v_states = ggml_cont(ctx0, v_states);94            cb(v_states, "v_states", il);95 96            v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v() * n_head, n_tokens,97                    ggml_row_size(kv->type, hparams.n_embd_head_v() * n_head),98                    0);99            cb(v_states, "v_states", il);100 101            q_pe = ggml_rope_ext(102                    ctx0, q_pe, inp_pos, nullptr,103                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,104                    ext_factor, attn_factor, beta_fast, beta_slow105                    );106            cb(q_pe, "q_pe", il);107 108            // shared RoPE key109            k_pe = ggml_rope_ext(110                    ctx0, k_pe, inp_pos, nullptr,111                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,112                    ext_factor, attn_factor, beta_fast, beta_slow113                    );114            cb(k_pe, "k_pe", il);115 116            ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);117            cb(q_states, "q_states", il);118 119            ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);120            cb(k_states, "k_states", il);121 122            cur = build_attn(inp_attn,123                    model.layers[il].wo, NULL, model.layers[il].wo_s,124                    q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);125        }126        if (il == n_layer - 1 && inp_out_ids) {127            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);128            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);129        }130        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);131        cb(ffn_inp, "ffn_inp", il);132 133        cur = build_norm(ffn_inp,134                model.layers[il].ffn_norm, NULL,135                LLM_NORM_RMS, il);136        cb(cur, "ffn_norm", il);137 138        cur = build_ffn(cur,139                model.layers[il].ffn_up,   NULL, NULL,140                NULL, NULL, NULL,141                model.layers[il].ffn_down, NULL, NULL,142                NULL,143                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);144        cb(cur, "ffn_out", il);145 146        cur = ggml_add(ctx0, cur, ffn_inp);147 148        cur = build_cvec(cur, il);149        cb(cur, "l_out", il);150 151        // input for next layer152        inpL = cur;153    }154    cur = inpL;155 156    cur = build_norm(cur,157            model.output_norm, NULL,158            LLM_NORM_RMS, -1);159 160    cb(cur, "result_norm", -1);161    res->t_embd = cur;162 163    cur = build_lora_mm(model.output, cur);164 165    cb(cur, "result_output", -1);166    res->t_logits = cur;167 168    ggml_build_forward_expand(gf, cur);169}170