model: Add support for Nanbeige4.2 (#25994)

* support nanbeige4.2 model * fix * fix flake8 Lint check * fix loop bound check and drop redundant head_dim --------- Co-authored-by: root <lizongqiang@kanzhun.com>
2026-07-27 17:04:18 +02:00
parent 0324696b8e
commit b77d646751
10 changed files with 265 additions and 1 deletions
@@ -143,6 +143,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },
    { LLM_ARCH_TALKIE,           "talkie"           },
    { LLM_ARCH_MELLUM,           "mellum"           },
+    { LLM_ARCH_NANBEIGE,         "nanbeige"         },
    { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };

@@ -221,6 +222,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
    { LLM_KV_TOKEN_SHIFT_COUNT,                 "%s.token_shift_count"                 },
    { LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         "%s.interleave_moe_layer_step"         },
    { LLM_KV_FULL_ATTENTION_INTERVAL,           "%s.full_attention_interval"           },
+    { LLM_KV_NUM_LOOPS,                         "%s.num_loops"                         },
+    { LLM_KV_SKIP_LOOP_FINAL_NORM,              "%s.skip_loop_final_norm"              },

    { LLM_KV_ATTENTION_HEAD_COUNT,                   "%s.attention.head_count"                   },
    { LLM_KV_ATTENTION_HEAD_COUNT_KV,                "%s.attention.head_count_kv"                },
@@ -148,6 +148,7 @@ enum llm_arch {
    LLM_ARCH_EAGLE3,
    LLM_ARCH_MINIMAX_M3,
    LLM_ARCH_DFLASH,
+    LLM_ARCH_NANBEIGE,
    LLM_ARCH_UNKNOWN,
 };

@@ -226,6 +227,8 @@ enum llm_kv {
    LLM_KV_TOKEN_SHIFT_COUNT,
    LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
    LLM_KV_FULL_ATTENTION_INTERVAL,
+    LLM_KV_NUM_LOOPS,
+    LLM_KV_SKIP_LOOP_FINAL_NORM,

    LLM_KV_ATTENTION_HEAD_COUNT,
    LLM_KV_ATTENTION_HEAD_COUNT_KV,
@@ -2339,6 +2339,7 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
        model.arch == LLM_ARCH_QWEN35 ||
        model.arch == LLM_ARCH_QWEN35MOE ||
        model.arch == LLM_ARCH_DEEPSEEK4 ||
+        model.arch == LLM_ARCH_NANBEIGE ||
        model.arch == LLM_ARCH_MINIMAX_M3) {
        return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
    }
@@ -85,6 +85,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
            return new llama_model_stablelm(params);
        case LLM_ARCH_MELLUM:
            return new llama_model_mellum(params);
+        case LLM_ARCH_NANBEIGE:
+            return new llama_model_nanbeige(params);
        case LLM_ARCH_QWEN:
            return new llama_model_qwen(params);
        case LLM_ARCH_QWEN2:
@@ -2491,6 +2493,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_LLAMA_EMBED:
        case LLM_ARCH_MAINCODER:
        case LLM_ARCH_GLM_DSA:
+        case LLM_ARCH_NANBEIGE:
            return LLAMA_ROPE_TYPE_NORM;

        // the pairs of head values are offset by n_rot/2
@@ -424,6 +424,22 @@ struct llama_model_mellum : public llama_model_base {
    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };

+struct llama_model_nanbeige : public llama_model_base {
+    llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    int  n_loops = 1;
+    int  n_layer_phys = 0;
+    bool skip_loop_final_norm = false;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
 struct llama_model_qwen : public llama_model_base {
    llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {}
    void load_arch_hparams(llama_model_loader & ml) override;
@@ -0,0 +1,184 @@
+#include "models.h"
+
+void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    uint32_t n_loops_u = 1;
+    ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false);
+    GGML_ASSERT(n_loops_u >= 1);
+
+    skip_loop_final_norm = false;
+    ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false);
+
+    n_layer_phys = (int) hparams.n_layer();
+
+    // Bound-check before casting: signed int mul can overflow and bypass the guard.
+    GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS);
+    n_loops = (int) n_loops_u;
+
+    // Expand logical layer count before load_tensors() allocates layers / KV.
+    if (n_loops > 1) {
+        for (int j = 1; j < n_loops; ++j) {
+            for (int i = 0; i < n_layer_phys; ++i) {
+                const int dst = i + j * n_layer_phys;
+                hparams.n_head_arr[dst]    = hparams.n_head_arr[i];
+                hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i];
+                hparams.n_ff_arr[dst]      = hparams.n_ff_arr[i];
+                hparams.is_swa_impl[dst]   = hparams.is_swa_impl[i];
+                hparams.is_recr_impl[dst]  = hparams.is_recr_impl[i];
+            }
+        }
+        hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops);
+    }
+
+    type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer;
+    for (int i = 0; i < n_phys; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2},
+                TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+        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_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+    }
+
+    // Share physical weights across loops; each slot still has its own KV index.
+    if (n_loops > 1) {
+        for (int j = 1; j < n_loops; ++j) {
+            for (int i = 0; i < n_phys; ++i) {
+                layers[i + j * n_phys] = layers[i];
+            }
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) :
+        llm_graph_context(params) {
+    const auto & nb = static_cast<const llama_model_nanbeige &>(model);
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    const int n_phys  = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer;
+    const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1;
+
+    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();
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f
+        ? 1.0f / sqrtf(float(n_embd_head))
+        : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        {
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+                    n_embd_head, n_head, n_head_kv, il);
+
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow);
+
+            Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, rope_factors,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", 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, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   model.layers[il].ffn_up_s,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+
+        if (n_loops > 1 &&
+            ((il + 1) % n_phys) == 0 &&
+            (il + 1) < n_layer &&
+            !nb.skip_loop_final_norm) {
+            cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "loop_norm", il);
+            inpL = cur;
+        }
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur, model.output_s);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}