move assistant to separate file

Author: am17an

src/models/gemma4-assistant.cpp

@@ -0,0 +1,208 @@
+#include "models.h"
+
+void llama_model_gemma4_assistant::load_arch_hparams(llama_model_loader & ml) {
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+
+    uint32_t n_kv_shared_layers = 0;
+    ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
+
+    hparams.n_layer_kv_from_start = hparams.n_layer - (int32_t) n_kv_shared_layers;
+    hparams.f_attention_scale     = 1.0f;
+
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,         hparams.nextn_predict_layers, false);
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,           hparams.rope_freq_base_train_swa, false);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,     hparams.n_swa);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,  hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA,     hparams.n_embd_head_k_swa);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,   hparams.n_embd_head_v_swa);
+
+    if (hparams.n_layer == 4) {
+        type = LLM_TYPE_31B;
+    }
+}
+
+void llama_model_gemma4_assistant::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    if (n_embd_head_k != n_embd_head_v) {
+        throw std::runtime_error("Gemma 4 assistant requires n_embd_head_k == n_embd_head_v");
+    }
+    if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
+        throw std::runtime_error("Gemma 4 assistant requires n_embd_head_k_swa == n_embd_head_v_swa");
+    }
+    if (hparams.n_embd_out() == n_embd) {
+        throw std::runtime_error("Gemma 4 assistant requires embedding_length_out to carry the target hidden size");
+    }
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+    output   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+
+    const int64_t n_embd_backbone = hparams.n_embd_out();
+    nextn_pre_proj  = create_tensor(tn(LLM_TENSOR_NEXTN_PRE_PROJ,  "weight"), { 2*n_embd_backbone, n_embd }, 0);
+    nextn_post_proj = create_tensor(tn(LLM_TENSOR_NEXTN_POST_PROJ, "weight"), { n_embd, n_embd_backbone }, 0);
+
+    int rope_freqs_flag = 0;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        const int64_t n_head      = hparams.n_head(i);
+        const int64_t n_embd_head = hparams.n_embd_head_k(i);
+        const int64_t n_ff        = hparams.n_ff(i);
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+        layer.wq        = create_tensor(tn(LLM_TENSOR_ATTN_Q,    "weight", i), { n_embd, n_embd_head*n_head }, 0);
+        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,  "weight", i), { n_embd_head*n_head, n_embd }, 0);
+
+        layer.attn_q_norm    = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM,    "weight", i), { n_embd_head }, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+        layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), { 1u }, 0);
+
+        if (!hparams.is_swa(i)) {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_embd_head/2 }, rope_freqs_flag);
+            rope_freqs_flag = TENSOR_DUPLICATED;
+        }
+
+        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), { n_embd }, 0);
+        layer.ffn_gate      = create_tensor(tn(LLM_TENSOR_FFN_GATE,      "weight", i), { n_embd, n_ff }, 0);
+        layer.ffn_up        = create_tensor(tn(LLM_TENSOR_FFN_UP,        "weight", i), { n_embd, n_ff }, 0);
+        layer.ffn_down      = create_tensor(tn(LLM_TENSOR_FFN_DOWN,      "weight", i), { n_ff, n_embd }, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), { n_embd }, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_gemma4_assistant::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_gemma4_assistant::graph::graph(const llama_model & model, const llm_graph_params & params) :
+        llm_graph_context(params) {
+    GGML_ASSERT(src_mctx  && "Gemma 4 assistant graph requires an MTP source (llama_set_mtp_source)");
+    GGML_ASSERT(src_model && "Gemma 4 assistant graph requires a source model");
+    GGML_ASSERT(src_model->tok_embd && "source model missing tok_embd");
+
+    const auto & src_hparams = src_model->hparams;
+
+    // By convention the MTP draft reads from the trunk's final SWA and full layers.
+    const int32_t src_layer_full = (int32_t) src_hparams.n_layer - 1;
+    const int32_t src_layer_swa  = (int32_t) src_hparams.n_layer - 2;
+    GGML_ASSERT(!src_hparams.is_swa(src_layer_full) && "trunk's last layer must be full attention");
+    GGML_ASSERT( src_hparams.is_swa(src_layer_swa)  && "trunk's penultimate layer must be SWA");
+
+    const int64_t n_embd_backbone = hparams.n_embd_out();
+
+    ggml_tensor * inp_tokens;
+    ggml_tensor * inp_h;
+    {
+        auto inp = std::make_unique<llm_graph_input_embd>(n_embd_backbone);
+
+        inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
+        cb(inp->tokens, "inp_tokens", -1);
+        ggml_set_input(inp->tokens);
+        inp_tokens = inp->tokens;
+        res->t_inp_tokens = inp->tokens;
+
+        inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd_backbone, ubatch.n_tokens);
+        cb(inp->embd, "inp_h", -1);
+        ggml_set_input(inp->embd);
+        inp_h = inp->embd;
+        res->t_inp_embd = inp->embd;
+
+        res->add_input(std::move(inp));
+    }
+
+    ggml_tensor * x = ggml_get_rows(ctx0, src_model->tok_embd, inp_tokens);
+    x = ggml_scale(ctx0, x, sqrtf((float) n_embd_backbone));
+    cb(x, "inp_embd_target", -1);
+
+    ggml_tensor * xh = ggml_concat(ctx0, x, inp_h, 0);
+    cb(xh, "inp_xh", -1);
+
+    ggml_tensor * cur = ggml_mul_mat(ctx0, model.nextn_pre_proj, xh);
+    cb(cur, "pre_proj", -1);
+
+    auto *        inp_attn    = build_attn_inp_src_kv_iswa();
+    ggml_tensor * inp_pos     = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    ggml_tensor * inpL = cur;
+
+    for (int il = 0; il < n_layer; ++il) {
+        const bool    is_swa = hparams.is_swa(il);
+        const int32_t il_src = is_swa ? src_layer_swa : src_layer_full;
+
+        const int64_t n_embd_head = hparams.n_embd_head_k(il);
+        const int64_t n_head      = hparams.n_head(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);
+
+        ggml_tensor * cur_norm = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur_norm, "attn_norm", il);
+
+        ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur_norm);
+        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);
+
+        ggml_tensor * freq_factors = is_swa ? nullptr : model.layers[il].rope_freqs;
+        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);
+
+        cur = build_attn(inp_attn, model.layers[il].wo, nullptr, nullptr,
+                Qcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il, il_src);
+
+        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);
+
+        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, nullptr,
+                model.layers[il].ffn_gate, nullptr, nullptr,
+                model.layers[il].ffn_down, nullptr, nullptr,
+                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);
+
+        cur = ggml_add(ctx0, cur, attn_out);
+
+        cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
+        cb(cur, "out_scaled", il);
+
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+
+    ggml_tensor * logits = build_lora_mm(model.output, cur);
+    cb(logits, "result_output", -1);
+    res->t_logits = logits;
+
+    ggml_tensor * h_next = ggml_mul_mat(ctx0, model.nextn_post_proj, cur);
+    cb(h_next, "result_h_pre_norm", -1);
+    res->t_h_pre_norm = h_next;
+
+    ggml_build_forward_expand(gf, logits);
+    ggml_build_forward_expand(gf, h_next);
+}