echodict/llama.cpp

1#include "models.h"2 3llm_build_starcoder2::llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {4    const int64_t n_embd_head = hparams.n_embd_head_v();5 6    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());7    GGML_ASSERT(n_embd_head == n_rot);8 9    ggml_tensor * cur;10    ggml_tensor * inpL;11 12    inpL = build_inp_embd(model.tok_embd);13 14    // inp_pos - contains the positions15    ggml_tensor * inp_pos = build_inp_pos();16 17    auto * inp_attn = build_attn_inp_kv();18 19    ggml_tensor * inp_out_ids = build_inp_out_ids();20 21    for (int il = 0; il < n_layer; ++il) {22        ggml_tensor * inpSA = inpL;23 24        // norm25        cur = build_norm(inpL,26                model.layers[il].attn_norm, model.layers[il].attn_norm_b,27                LLM_NORM, il);28        cb(cur, "attn_norm", il);29 30        // self-attention31        {32            // compute Q and K and RoPE them33            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,34                    n_embd_head, n_head, n_head_kv, il);35 36            Qcur = ggml_rope_ext(37                    ctx0, Qcur, inp_pos, nullptr,38                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,39                    ext_factor, attn_factor, beta_fast, beta_slow40                    );41 42            Kcur = ggml_rope_ext(43                    ctx0, Kcur, inp_pos, nullptr,44                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,45                    ext_factor, attn_factor, beta_fast, beta_slow46                    );47 48            cb(Qcur, "Qcur", il);49            cb(Kcur, "Kcur", il);50            cb(Vcur, "Vcur", il);51 52            cur = build_attn(inp_attn,53                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,54                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);55        }56        if (il == n_layer - 1 && inp_out_ids) {57            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);58            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);59        }60        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);61        cb(ffn_inp, "ffn_inp", il);62 63        // feed-forward network64 65        cur = build_norm(ffn_inp,66                model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,67                LLM_NORM, il);68        cb(cur, "ffn_norm", il);69 70        cur = build_ffn(cur,71                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,72                NULL,                      NULL,                        NULL,73                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,74                NULL,75                LLM_FFN_GELU, LLM_FFN_SEQ, il);76        cb(cur, "ffn_out", il);77 78        cur = ggml_add(ctx0, cur, ffn_inp);79 80        cur = build_cvec(cur, il);81        cb(cur, "l_out", il);82 83        // input for next layer84        inpL = cur;85    }86    cur = inpL;87 88    cur = build_norm(cur,89            model.output_norm, model.output_norm_b,90            LLM_NORM, -1);91 92    cb(cur, "result_norm", -1);93    res->t_embd = cur;94 95    // lm_head96    cur = build_lora_mm(model.output, cur);97 98    cb(cur, "result_output", -1);99    res->t_logits = cur;100 101    ggml_build_forward_expand(gf, cur);102}103