[Model] Support for Spark2_5ForCausalLM implementation (#27868)

* Add Spark3 Model
* rename spark3 -> spark2_5

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: dongjiang <dongjiang2010@gmail.com>

src/llama-arch.cpp

@@ -146,6 +146,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_PADDLEOCR,        "paddleocr"        },
     { LLM_ARCH_MIMO2,            "mimo2"            },
     { LLM_ARCH_STEP35,           "step35"           },
+    { LLM_ARCH_SPARK2_5,         "spark2_5"         },
     { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
     { LLM_ARCH_MAINCODER,        "maincoder"        },
     { LLM_ARCH_KIMI_LINEAR,      "kimi-linear"      },

src/llama-arch.h

@@ -147,6 +147,7 @@ enum llm_arch {
     LLM_ARCH_PADDLEOCR,
     LLM_ARCH_MIMO2,
     LLM_ARCH_STEP35,
+    LLM_ARCH_SPARK2_5,
     LLM_ARCH_LLAMA_EMBED,
     LLM_ARCH_MAINCODER,
     LLM_ARCH_KIMI_LINEAR,

src/llama-model-saver.cpp

@@ -27,6 +27,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
         case LLM_ARCH_APERTUS:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
+        case LLM_ARCH_SPARK2_5:
         case LLM_ARCH_MUSE_GLIMMER:
         case LLM_ARCH_MELLUM:
         case LLM_ARCH_LAGUNA:

src/llama-model.cpp

@@ -338,6 +338,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_kimi_k3(params);
         case LLM_ARCH_STEP35:
             return new llama_model_step35(params);
+        case LLM_ARCH_SPARK2_5:
+            return new llama_model_spark2_5(params);
         default:
             throw std::runtime_error(std::string("unsupported model architecture: '") + llm_arch_name(arch) + "'");
     }
@@ -2999,6 +3001,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
+        case LLM_ARCH_SPARK2_5:
         case LLM_ARCH_TALKIE:
         case LLM_ARCH_MELLUM:
             return LLAMA_ROPE_TYPE_NEOX;

src/llama-vocab.cpp

@@ -325,6 +325,14 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_SPARK2_5:
+                regex_exprs = {
+                    "\\p{N}{1,3}",
+                    "[一-龥぀-ゟ゠-ヿ]+",
+                    "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+|[\r\n]|\\s+(?!\\S)|\\s+",
+                    "\\p{N}",
+                };
+                break;
             case LLAMA_VOCAB_PRE_TYPE_YOUTU:
                 regex_exprs = {
                     "[가-힣ㄱ-ㆎ]+|[！…“”‘’—：；，、-〿︰-﹏]+|[ㄅ-ㄯ]+|[一-龥぀-ゟ゠-ヿ]+",
@@ -2170,6 +2178,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "deepseek-v3") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
                 clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "spark2_5") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SPARK2_5;
+                clean_spaces = false;
             } else if (
                     tokenizer_pre == "youtu") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_YOUTU;

src/llama-vocab.h

@@ -66,6 +66,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_MELLUM2           = 55,
     LLAMA_VOCAB_PRE_TYPE_LAGUNA            = 56,
     LLAMA_VOCAB_PRE_TYPE_HY_V4             = 57,
+    LLAMA_VOCAB_PRE_TYPE_SPARK2_5          = 58,
 };
 
 struct LLM_KV;

src/models/models.h

@@ -2606,3 +2606,16 @@ struct llama_model_step35 : public llama_model_base {
 
     std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
+
+
+struct llama_model_spark2_5 : public llama_model_base {
+    llama_model_spark2_5(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;
+
+    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;
+};

src/models/spark2-5.cpp

@@ -0,0 +1,146 @@
+#include "models.h"
+
+void llama_model_spark2_5::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
+
+    hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
+    hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+
+    switch (hparams.n_layer()) {
+        case 28: type = LLM_TYPE_1_7B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_spark2_5::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 == nullptr) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        const int64_t n_head_i = hparams.n_head(i);
+        const int64_t n_head_kv_i = hparams.n_head_kv(i);
+        const int64_t n_embd_q = hparams.n_embd_head_k(i) * n_head_i;
+        const int64_t n_embd_k = hparams.n_embd_head_k(i) * n_head_kv_i;
+        const int64_t n_embd_v = hparams.n_embd_head_v(i) * n_head_kv_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_q, n_embd_k, n_embd_v, 0);
+        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_i}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q, n_embd}, 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_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);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_spark2_5::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_spark2_5::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+    GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_STANDARD);
+
+    ggml_tensor * 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();
+
+    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+        ggml_tensor * cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        const int64_t n_head_i = hparams.n_head(il);
+        const int64_t n_head_kv_i = hparams.n_head_kv(il);
+        const int64_t n_rot_i = hparams.n_rot(il);
+        const float freq_base_i = model.get_rope_freq_base(cparams, il);
+        const float freq_scale_i = model.get_rope_freq_scale(cparams, il);
+
+        ggml_tensor * attn_inp = cur;
+        auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head_i, n_head_kv_i, il);
+
+        Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
+                n_rot_i, rope_type, n_ctx_orig, freq_base_i, freq_scale_i,
+                ext_factor, attn_factor, beta_fast, beta_slow);
+        Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
+                n_rot_i, rope_type, n_ctx_orig, freq_base_i, freq_scale_i,
+                ext_factor, attn_factor, beta_fast, beta_slow);
+        cb(Qcur, "Qcur_rope", il);
+        cb(Kcur, "Kcur_rope", il);
+
+        cur = build_attn(inp_attn,
+                nullptr, nullptr, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+        cb(cur, "attn_out", il);
+
+        ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
+        gate = ggml_sigmoid(ctx0, gate);
+        cb(gate, "attn_gate", il);
+
+        const int64_t n_tokens_i = cur->ne[1];
+        cur = ggml_reshape_3d(ctx0, cur, n_embd_head, n_head_i, n_tokens_i);
+        gate = ggml_reshape_3d(ctx0, gate, 1, n_head_i, n_tokens_i);
+        cur = ggml_mul(ctx0, cur, gate);
+        cur = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_i, n_tokens_i);
+        cb(cur, "attn_gated", il);
+
+        cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
+        cb(cur, "attn_out_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);
+
+        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 = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    ggml_tensor * cur = build_norm(inpL, 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);
+}