vshmidt/llama.cpp: LLM inference in C/C++

src/models/mpt.cpp
106 строк3 KB
PikaPikachu
model : refactor QKV into common build_qkv and create_tensor_qkv helpers (#21245)
16 апр 2026, 18:41
16 апр 2026, 18:419db77a0
#include "models.h"

llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
    const int64_t n_embd_head = hparams.n_embd_head_v();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());

    ggml_tensor * cur;
    ggml_tensor * pos;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    auto * inp_attn = build_attn_inp_kv();

    if (model.pos_embd) {
        // inp_pos - contains the positions
        ggml_tensor * inp_pos = build_inp_pos();
        pos                   = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
        cb(pos, "pos_embd", -1);

        inpL = ggml_add(ctx0, inpL, pos);
        cb(inpL, "inpL", -1);
    }

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    for (int il = 0; il < n_layer; ++il) {
        ggml_tensor * attn_norm;

        attn_norm = build_norm(inpL, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, il);
        cb(attn_norm, "attn_norm", il);

        // self-attention
        {
            cur = attn_norm;

            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
                    n_embd_head, n_head, n_head_kv, il);

            // Q/K Layernorm
            if (model.layers[il].attn_q_norm) {
                Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head * n_head, n_tokens);
                Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head * n_head_kv, n_tokens);

                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, model.layers[il].attn_q_norm_b, LLM_NORM, il);

                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, model.layers[il].attn_k_norm_b, LLM_NORM, il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
            }

            cb(Qcur, "Qcur", il);
            cb(Kcur, "Kcur", il);
            cb(Vcur, "Vcur", il);

            cur = build_attn(inp_attn,
                    model.layers[il].wo, model.layers[il].bo, model.layers[il].wo_s,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
        }

        if (il == n_layer - 1 && inp_out_ids) {
            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);
            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
        }

        // Add the input
        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
        cb(ffn_inp, "ffn_inp", il);

        // feed forward
        {
            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, il);
            cb(cur, "ffn_norm", il);
            cur = build_ffn(cur,
                model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
                NULL, NULL, NULL,
                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                model.layers[il].ffn_act, LLM_FFN_GELU, LLM_FFN_SEQ, il);
            cb(cur, "ffn_out", il);
        }

        cur = ggml_add(ctx0, cur, ffn_inp);

        cur = build_cvec(cur, il);
        cb(cur, "l_out", il);

        // input for next layer
        inpL = cur;
    }

    cur = inpL;

    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);

    cb(cur, "result_norm", -1);
    res->t_embd = cur;

    cur = build_lora_mm(model.output, cur);

    cb(cur, "result_output", -1);
    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);
}