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

src/models/step35-iswa.cpp
167 строк7 KB
Sigbjørn Skjæret
model : support NVFP4 tensors for Gemma4 (#21971)
16 апр 2026, 17:51
16 апр 2026, 17:51f772f6e
#include "models.h"

llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);
    ggml_tensor * inp_pos     = build_inp_pos();
    auto        * inp_attn    = build_attn_inp_kv_iswa();
    ggml_tensor * inp_out_ids = build_inp_out_ids();

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

        const uint32_t n_head_l    = hparams.n_head(il);
        const uint32_t n_head_kv_l = hparams.n_head_kv(il);

        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);

        cur = inpL;

        // dump pre-attn RMSNorm input to pinpoint layer boundary issues
        cb(cur, "attn_norm_in", il);

        // self-attention
        {
            cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
            cb(cur, "attn_norm", il);
            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);

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

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);

            // Q/K per-head RMSNorm (Step35 q_norm / k_norm)
            if (model.layers[il].attn_q_norm) {
                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
                cb(Qcur, "Qcur_normed", il);
            }
            if (model.layers[il].attn_k_norm) {
                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
                cb(Kcur, "Kcur_normed", il);
            }

            // RoPE (partial rotary factors per layer)
            const bool is_swa = hparams.is_swa(il);
            ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
            const int64_t n_rot_l = hparams.n_rot(il);
            Qcur = ggml_rope_ext(
                ctx0, Qcur, inp_pos, rope_factors,
                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                ext_factor, attn_factor, beta_fast, beta_slow
            );
            Kcur = ggml_rope_ext(
                ctx0, Kcur, inp_pos, rope_factors,
                n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                ext_factor, attn_factor, beta_fast, beta_slow
            );
            cb(Qcur, "Qcur_pos", il);
            cb(Kcur, "Kcur_pos", il);

            const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
            ggml_tensor * attn_out = build_attn(inp_attn,
                    nullptr, nullptr, nullptr,
                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
            cb(attn_out, "attn_out", il);
            // head-wise attention gate: sigmoid(g_proj(x)) in torch
            if (model.layers[il].wqkv_gate) {
                ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]
                cb(gate, "attn_gate", il);

                gate = ggml_sigmoid(ctx0, gate);
                cb(gate, "attn_gate_sigmoid", il);

                // reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]
                ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
                ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate,       1,          n_head_l, n_tokens);
                cb(gate_3d, "attn_gate_3d", il);

                attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
                cb(attn_3d, "attn_gated_3d", il);

                attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
                cb(attn_out, "attn_gated", il);
            }

            // output projection
            cur = build_lora_mm(model.layers[il].wo, attn_out, model.layers[il].wo_s);
            cb(cur, "attn_proj", il);
        }

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

        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
        cb(ffn_inp, "ffn_inp", il);

        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
        cb(cur, "ffn_norm", il);

        // feed-forward
        if (model.layers[il].ffn_gate_inp == nullptr) {
            // dense MLP
            cur = build_ffn(cur,
                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   nullptr,
                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
                    nullptr,
                    LLM_FFN_SILU, LLM_FFN_PAR, il);
            cb(cur, "ffn_out", il);
        } else {
            // MoE routed experts
            ggml_tensor * moe_out = build_moe_ffn(cur,
                    model.layers[il].ffn_gate_inp,
                    model.layers[il].ffn_up_exps,
                    model.layers[il].ffn_gate_exps,
                    model.layers[il].ffn_down_exps,
                    model.layers[il].ffn_exp_probs_b,
                    n_expert, n_expert_used,
                    LLM_FFN_SILU, hparams.expert_weights_norm,
                    hparams.expert_weights_scale,
                    (llama_expert_gating_func_type) hparams.expert_gating_func,
                    il);
            cb(moe_out, "ffn_moe_out", il);

            // shared expert MLP (always added on MoE layers in Step35)
            ggml_tensor * sh_out = build_ffn(cur,
                    model.layers[il].ffn_up_shexp,   nullptr, nullptr,
                    model.layers[il].ffn_gate_shexp, nullptr, nullptr,
                    model.layers[il].ffn_down_shexp, nullptr, nullptr,
                    nullptr,
                    LLM_FFN_SILU, LLM_FFN_PAR, il);
            cb(sh_out, "ffn_shared_out", il);

            cur = ggml_add(ctx0, moe_out, sh_out);
            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, nullptr, LLM_NORM_RMS, -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);
}