slm_ikllama_sys 0.1.1

ik_llama.cpp rust sys bindings
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#include "../llama-build-context.h"
#include "../llama-model.h"
#include "../llama-context.h"

ggml_cgraph * llm_build_context::build_command_r() {

    ggml_cgraph * gf = new_graph_custom();

    const int64_t n_embd_head = hparams.n_embd_head_v(0);
    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
    const float f_logit_scale = hparams.f_logit_scale;

    struct ggml_tensor * cur;
    struct ggml_tensor * inpL;

    inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

    // inp_pos - contains the positions
    struct ggml_tensor * inp_pos = build_inp_pos();

    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
    struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

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

        // self-attention (norm applied inside; handles graph-parallel split path automatically)
        auto attn_out = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos,
                nullptr, nullptr, KQ_mask, nullptr, nullptr,
                1.0f / sqrtf(float(n_embd_head)), 0.f,
                0, il, /*do_rope=*/true, /*add_graph_split=*/true,
                /*add_input=*/true, /*is_norm=*/true, /*is_multi=*/false);
        cb(attn_out, "attn_out", il);

        if (il == n_layer - 1 && n_tokens > 1) {
            // skip computing output for unused tokens
            struct ggml_tensor * inp_out_ids = build_inp_out_ids();
            attn_out = ggml_get_rows(ctx0, attn_out, inp_out_ids);
            inpL     = ggml_get_rows(ctx0, inpL,     inp_out_ids);
        }

        attn_out->op_params[3] = 1; // turn off attention reduce; the FFN reduce will cover both

        // feed-forward network (norm applied inside; graph-parallel when ffn tensors are split)
        cur = llm_build_ffn(ctx0, lctx, model.layers[il].attn_norm, inpL,
                model.layers[il].ffn_up,   NULL, NULL,
                model.layers[il].ffn_gate, NULL, NULL,
                model.layers[il].ffn_down, NULL, NULL,
                NULL,
                LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, /*add_input=*/false, /*is_norm=*/true, attn_out);
        cb(cur, "ffn_out", il);

        cur = lctx.cvec.apply_to(ctx0, cur, il);
        cb(cur, "l_out", il);

        // input for next layer
        inpL = cur;
    }

    cur = inpL;

    cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM, cb, -1);
    cb(cur, "result_norm", -1);

    if (f_logit_scale) {
        cur = ggml_scale(ctx0, cur, f_logit_scale);
        cb(cur, "result_norm_scaled", -1);
    }

    // lm_head
    cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
    cb(cur, "result_output", -1);

    ggml_build_forward_expand(gf, cur);

    return gf;

}