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

src/models/gemma4-iswa.cpp
322 строки13 KB
Sigbjørn Skjæret
model : support NVFP4 tensors for Gemma4 (#21971)
16 апр 2026, 17:51
16 апр 2026, 17:51f772f6e
#include "models.h"

// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {
    GGML_ASSERT(idx < (int) x->ne[2]);
    return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
                        idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
}

llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params) :
        llm_graph_context(params),
        model(model),
        n_embd_per_layer(model.hparams.n_embd_per_layer) {
    ggml_tensor * cur;
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
    cb(inpL, "inp_scaled", -1);

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

    // TODO: is causal == true correct? might need some changes
    auto * inp_attn = build_attn_inp_kv_iswa();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    ggml_tensor * inp_per_layer = nullptr;
    if (model.per_layer_tok_embd) {
        inp_per_layer = build_inp_per_layer();
        ggml_build_forward_expand(gf, inp_per_layer);

        // inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer]
        inp_per_layer = project_per_layer_inputs(inpL, inp_per_layer);
    }

    for (int il = 0; il < n_layer; ++il) {
        const int64_t n_embd_head = hparams.n_embd_head_k(il);
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_v(il));

        const int64_t n_head    = hparams.n_head(il);
        const int64_t n_head_kv = 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);
        const int   n_rot_l      = hparams.n_rot(il);

        // norm
        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
        cb(cur, "attn_norm", il);

        ggml_tensor * freq_factors = nullptr;
        if (!hparams.is_swa(il)) {
            // full_attention layers use rope_freqs for proportional rope
            freq_factors = model.layers[il].rope_freqs;
        }

        // Q projection (shared for both non-KV and KV layers)
        // this is to mirror Gemma4Attention in pytorch code
        ggml_tensor * Qcur;
        {
            Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
            cb(Qcur, "Qcur", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
            cb(Qcur, "Qcur_normed", il);

            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, freq_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);
        }

        // self-attention
        if (hparams.has_kv(il)) {
            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
            cb(Kcur, "Kcur", il);

            ggml_tensor * Vcur = model.layers[il].wv
                                    ? build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s)
                                    : Kcur; // if v_proj is not present, use Kcur as Vcur
            cb(Vcur, "Vcur", il);

            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
            Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);

            cb(Kcur, "Kcur_normed", il);
            cb(Vcur, "Vcur_normed", il);

            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                                 ext_factor, attn_factor, beta_fast, beta_slow);

            cb(Kcur, "Kcur_pos", il);

            cur = build_attn(inp_attn, model.layers[il].wo,
                    nullptr, model.layers[il].wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
                    hparams.f_attention_scale, il);
        } else {
            // reuse KV cache of earlier layers
            cur = build_attn(inp_attn,
                    model.layers[il].wo, nullptr, model.layers[il].wo_s,
                    Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
        }

        // TODO @ngxson : strip unused token right after the last KV layer to speed up prompt processing
        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);
        }
        cur = build_norm(cur,
                model.layers[il].attn_post_norm, nullptr,
                LLM_NORM_RMS, il);
        cb(cur, "attn_post_norm", il);

        ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);
        cb(attn_out, "attn_out", il);

        // feed-forward network
        const bool is_moe_layer = model.layers[il].ffn_gate_inp != nullptr;
        if (is_moe_layer) {
            // MLP (shared exp)
            ggml_tensor * cur_mlp = build_norm(attn_out,
                    model.layers[il].ffn_norm, nullptr,
                    LLM_NORM_RMS, il);
            cb(cur_mlp, "ffn_norm_1", il);

            cur_mlp = build_ffn(cur_mlp,
                    model.layers[il].ffn_up,   nullptr, model.layers[il].ffn_up_s,
                    model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s,
                    model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
                    nullptr,
                    LLM_FFN_GELU, LLM_FFN_PAR, il);
            cur_mlp = build_norm(cur_mlp,
                    model.layers[il].ffn_post_norm_1, nullptr,
                    LLM_NORM_RMS, il);
            cb(cur_mlp, "ffn_mlp", il);

            // Expert FFN
            ggml_tensor * cur_moe = build_norm(attn_out,
                    model.layers[il].ffn_pre_norm_2, nullptr,
                    LLM_NORM_RMS, il);
            cb(cur_moe, "ffn_norm_2", il);

            // custom MoE logits calculation (router operates on attn_out, not cur)
            ggml_tensor * tmp = ggml_rms_norm(ctx0, attn_out, hparams.f_norm_rms_eps);
            tmp = ggml_scale(ctx0, tmp, 1.0f / sqrtf((float) n_embd));
            tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_gate_inp_s);
            ggml_tensor * logits = build_lora_mm(model.layers[il].ffn_gate_inp, tmp); // [n_expert, n_tokens]
            cb(logits, "ffn_moe_logits", il);

            cur_moe = build_moe_ffn(cur_moe,
                    nullptr, // gate_inp
                    nullptr, // up_exps
                    nullptr, // gate_exps
                    model.layers[il].ffn_down_exps,
                    nullptr, // exp_probs_b (not used for gemma4)
                    n_expert, n_expert_used,
                    LLM_FFN_GELU, true,
                    1.0f,
                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                    il, logits,
                    model.layers[il].ffn_gate_up_exps,
                    nullptr, // up_exps_s
                    nullptr, // gate_exps_s
                    model.layers[il].ffn_down_exps_s);
            cur_moe = build_norm(cur_moe,
                    model.layers[il].ffn_post_norm_2, nullptr,
                    LLM_NORM_RMS, il);
            cb(cur_moe, "ffn_moe", il);

            cur = ggml_add(ctx0, cur_mlp, cur_moe);
            cb(cur, "ffn_moe_combined", il);
        } else {
            cur = build_norm(attn_out,
                    model.layers[il].ffn_norm, nullptr,
                    LLM_NORM_RMS, il);
            cb(cur, "ffn_norm", il);

            cur = build_ffn(cur,
                    model.layers[il].ffn_up,   nullptr, model.layers[il].ffn_up_s,
                    model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s,
                    model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
                    nullptr,
                    LLM_FFN_GELU, LLM_FFN_PAR, il);
            cb(cur, "ffn_out", il);
        }
        cur = build_norm(cur,
                model.layers[il].ffn_post_norm, nullptr,
                LLM_NORM_RMS, -1);
        cb(cur, "ffn_post_norm", il);

        // residual connection
        cur = ggml_add(ctx0, cur, attn_out);

        // per-layer embedding
        if (inp_per_layer) {
            ggml_tensor * pe_in = cur;
            cb(cur, "pe_in", il);

            cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]
            cur = ggml_gelu(ctx0, cur);

            ggml_tensor * inp_this_layer = ggml_view_2d_slice(ctx0, inp_per_layer, il); // [n_embd_per_layer, n_tokens]

            // TODO @ngxson : improve this
            if (il == n_layer - 1 && inp_out_ids) {
                inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);
            }

            cur = ggml_mul(ctx0, cur, inp_this_layer);
            cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens]
            cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il);
            cb(cur, "per_layer_embd_out", il);

            // residual connection
            cur = ggml_add(ctx0, pe_in, cur);
        }

        // layer_scalar
        if (model.layers[il].out_scale) {
            cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
            cb(cur, "out_scaled", il);
        }

        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;

    // lm_head
    cur = build_lora_mm(model.output, cur);

    if (hparams.f_final_logit_softcapping) {
        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
        cur = ggml_tanh(ctx0, cur);
        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
    }

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

    ggml_build_forward_expand(gf, cur);
}

// equivalent to get_per_layer_inputs() in python code
// output shape: [n_embd_per_layer, n_layer, n_tokens]
ggml_tensor * llm_build_gemma4_iswa::build_inp_per_layer() {
    auto inp = std::make_unique<llm_graph_input_embd>(n_embd);

    ggml_tensor * inp_per_layer;
    float tok_embd_scale = sqrtf((float) n_embd_per_layer);
    if (ubatch.token) {
        inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
        ggml_set_input(inp->tokens);
        res->t_inp_tokens = inp->tokens;

        inp_per_layer = ggml_get_rows  (ctx0, model.per_layer_tok_embd, inp->tokens);
        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, n_tokens);
        inp_per_layer = ggml_scale     (ctx0, inp_per_layer, tok_embd_scale);
        cb(inp_per_layer, "inp_per_layer_selected", -1);

        res->add_input(std::move(inp));
    } else {
        // Multimodal embedding path: use padding token (ID=0) embedding
        // TODO: verify if this is the correct behavior in transformers implementation
        const int64_t embd_size = model.per_layer_tok_embd->ne[0];  // n_embd_per_layer * n_layer

        // Extract and dequantize padding token embedding (row 0)
        ggml_tensor * padding = ggml_view_1d(ctx0, model.per_layer_tok_embd, embd_size, 0);
        inp_per_layer = ggml_cast (ctx0, padding, GGML_TYPE_F32);
        inp_per_layer = ggml_scale(ctx0, inp_per_layer, tok_embd_scale);

        // Reshape to [n_embd_per_layer, n_layer, 1]
        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, 1);
        cb(inp_per_layer, "inp_per_layer_multimodal", -1);
    }
    return inp_per_layer;
}

// equivalent to project_per_layer_inputs() in python code
// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
// inp_batch     shape: [n_embd, n_tokens]
// inp_per_layer shape: [n_embd_per_layer, n_layer, n_tokens] (from build_inp_per_layer)
// output shape: [n_embd_per_layer, n_tokens, n_layer]
ggml_tensor * llm_build_gemma4_iswa::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {
    const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
    const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);

    // note: this matrix multiplication will be performed in the input layer (i.e. on the CPU)
    ggml_tensor * per_layer_proj;
    per_layer_proj = ggml_mul_mat   (ctx0, model.per_layer_model_proj, inp_batch);
    per_layer_proj = ggml_scale     (ctx0, per_layer_proj, per_layer_projection_scale);
    per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_per_layer, n_layer, n_tokens);

    per_layer_proj = build_norm(per_layer_proj, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS, -1);
    cb(per_layer_proj, "per_layer_proj", -1);

    inp_per_layer = ggml_add  (ctx0, per_layer_proj, inp_per_layer);
    inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
    cb(inp_per_layer, "inp_per_layer", -1);

    // permute to shape: [n_embd_per_layer, n_tokens, n_layer]
    inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
    return inp_per_layer;
}