talk-llama : sync llama.cpp

diff --git a/examples/talk-llama/llama-arch.cpp b/examples/talk-llama/llama-arch.cpp
index 4e8d54c4193cc107e82a7e00c40d1fdcc0bcbc9c..869e4dccf0dc9922bfb41f9476108b2d3cbbb6d5 100644 (file)
--- a/examples/talk-llama/llama-arch.cpp
+++ b/examples/talk-llama/llama-arch.cpp
@@ -93,12 +93,14 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_SMOLLM3,          "smollm3"          },
     { LLM_ARCH_OPENAI_MOE,       "gpt-oss"          },
     { LLM_ARCH_LFM2,             "lfm2"             },
+    { LLM_ARCH_LFM2MOE,          "lfm2moe"          },
     { LLM_ARCH_DREAM,            "dream"            },
     { LLM_ARCH_SMALLTHINKER,     "smallthinker"     },
     { LLM_ARCH_LLADA,            "llada"            },
     { LLM_ARCH_LLADA_MOE,        "llada-moe"        },
     { LLM_ARCH_SEED_OSS,         "seed_oss"         },
     { LLM_ARCH_GROVEMOE,         "grovemoe"         },
+    { LLM_ARCH_APERTUS,          "apertus"          },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -217,6 +219,11 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
 
     { LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
+    // sentence-transformers dense modules feature dims
+    { LLM_KV_DENSE_2_FEAT_IN,        "%s.dense_2_feat_in"  },
+    { LLM_KV_DENSE_2_FEAT_OUT,       "%s.dense_2_feat_out"  },
+    { LLM_KV_DENSE_3_FEAT_IN,        "%s.dense_3_feat_in"   },
+    { LLM_KV_DENSE_3_FEAT_OUT,       "%s.dense_3_feat_out"  },
 
     { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
     { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
@@ -256,6 +263,11 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ADAPTER_LORA_PROMPT_PREFIX,      "adapter.lora.prompt_prefix" },
     { LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS, "adapter.alora.invocation_tokens" },
 
+    { LLM_KV_XIELU_ALPHA_N,         "xielu.alpha_n"         },
+    { LLM_KV_XIELU_ALPHA_P,         "xielu.alpha_p"         },
+    { LLM_KV_XIELU_BETA,            "xielu.beta"            },
+    { LLM_KV_XIELU_EPS,             "xielu.eps"             },
+
     // deprecated
     { LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
     { LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
@@ -1064,6 +1076,8 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
             { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_DENSE_2_OUT,     "dense_2" },
+            { LLM_TENSOR_DENSE_3_OUT,     "dense_3" },
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
             { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
@@ -2098,6 +2112,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_OUTPUT,            "output" },
         }
     },
+    {
+        LLM_ARCH_LFM2MOE,
+        {
+            { LLM_TENSOR_ATTN_NORM,         "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,            "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,            "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,            "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,          "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_K_NORM,       "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_Q_NORM,       "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_FFN_DOWN,          "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE,          "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_NORM,          "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,            "blk.%d.ffn_up" },
+            { LLM_TENSOR_SHORTCONV_CONV,    "blk.%d.shortconv.conv" },
+            { LLM_TENSOR_SHORTCONV_INPROJ,  "blk.%d.shortconv.in_proj" },
+            { LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
+            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
+            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,      "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,     "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,     "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,       "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_EXP_PROBS_B,   "blk.%d.exp_probs_b" },
+        }
+    },
     {
         LLM_ARCH_SMALLTHINKER,
         {
@@ -2119,6 +2159,25 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" }
         },
     },
+    {
+        LLM_ARCH_APERTUS,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_DREAM,
         {
@@ -2229,6 +2288,8 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_DENSE_2_OUT,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
+    {LLM_TENSOR_DENSE_3_OUT,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
     {LLM_TENSOR_OUTPUT_NORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_DEC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_ENC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
@@ -2468,6 +2529,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_PLAMO2:
         case LLM_ARCH_GRANITE_HYBRID:
         case LLM_ARCH_LFM2:
+        case LLM_ARCH_LFM2MOE:
         case LLM_ARCH_NEMOTRON_H:
             return true;
         default:

diff --git a/examples/talk-llama/llama-arch.h b/examples/talk-llama/llama-arch.h
index b5c6f3d76a62cba5c4c166ace9dceaa06b47c65b..c3ae71655b17b417acce9f052925296802ba21a2 100644 (file)
--- a/examples/talk-llama/llama-arch.h
+++ b/examples/talk-llama/llama-arch.h
@@ -97,12 +97,14 @@ enum llm_arch {
     LLM_ARCH_SMOLLM3,
     LLM_ARCH_OPENAI_MOE,
     LLM_ARCH_LFM2,
+    LLM_ARCH_LFM2MOE,
     LLM_ARCH_DREAM,
     LLM_ARCH_SMALLTHINKER,
     LLM_ARCH_LLADA,
     LLM_ARCH_LLADA_MOE,
     LLM_ARCH_SEED_OSS,
     LLM_ARCH_GROVEMOE,
+    LLM_ARCH_APERTUS,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -260,10 +262,21 @@ enum llm_kv {
 
     LLM_KV_SHORTCONV_L_CACHE,
 
+    LLM_KV_XIELU_ALPHA_N,
+    LLM_KV_XIELU_ALPHA_P,
+    LLM_KV_XIELU_BETA,
+    LLM_KV_XIELU_EPS,
+
     // deprecated:
     LLM_KV_TOKENIZER_PREFIX_ID,
     LLM_KV_TOKENIZER_SUFFIX_ID,
     LLM_KV_TOKENIZER_MIDDLE_ID,
+
+    // sentence-transformers dense layers in and out features
+    LLM_KV_DENSE_2_FEAT_IN,
+    LLM_KV_DENSE_2_FEAT_OUT,
+    LLM_KV_DENSE_3_FEAT_IN,
+    LLM_KV_DENSE_3_FEAT_OUT,
 };
 
 enum llm_tensor {
@@ -271,6 +284,8 @@ enum llm_tensor {
     LLM_TENSOR_TOKEN_EMBD_NORM,
     LLM_TENSOR_TOKEN_TYPES,
     LLM_TENSOR_POS_EMBD,
+    LLM_TENSOR_DENSE_2_OUT,
+    LLM_TENSOR_DENSE_3_OUT,
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
     LLM_TENSOR_ROPE_FREQS,

diff --git a/examples/talk-llama/llama-chat.cpp b/examples/talk-llama/llama-chat.cpp
index 66e6c6a38f1cd4ac29080aa41ea8fdd56dc4e494..956c4e085e5b6e10e787d61d9dcf4f31f9e6ff23 100644 (file)
--- a/examples/talk-llama/llama-chat.cpp
+++ b/examples/talk-llama/llama-chat.cpp
@@ -590,7 +590,7 @@ int32_t llm_chat_apply_template(
             ss << message->content << "<|end_of_text|>\n";
         }
         if (add_ass) {
-            ss << "<|start_of_role|>assistant<|end_of_role|>\n";
+            ss << "<|start_of_role|>assistant<|end_of_role|>";
         }
     } else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
         // GigaChat template

diff --git a/examples/talk-llama/llama-context.cpp b/examples/talk-llama/llama-context.cpp
index d8a8b5e647a8508303f2ff548db816141e0e781a..e7526e7d0a55791630066cad4ce0df075a9c8e64 100644 (file)
--- a/examples/talk-llama/llama-context.cpp
+++ b/examples/talk-llama/llama-context.cpp
@@ -2346,6 +2346,12 @@ llama_context * llama_init_from_model(
         return nullptr;
     }
 
+    if (params.pooling_type != model->hparams.pooling_type) {
+        //user-specified pooling-type is different from the model default
+        LLAMA_LOG_WARN("%s: model default pooling_type is [%d], but [%d] was specified\n", __func__,
+                       model->hparams.pooling_type, params.pooling_type);
+    }
+
     try {
         auto * ctx = new llama_context(*model, params);
         return ctx;

diff --git a/examples/talk-llama/llama-graph.cpp b/examples/talk-llama/llama-graph.cpp
index 90cd885a60a4f6801d61c7abf4ad610ead4f81a7..a24853c63ada4006f10b059a71e5bc22750ed2c0 100644 (file)
--- a/examples/talk-llama/llama-graph.cpp
+++ b/examples/talk-llama/llama-graph.cpp
@@ -1853,6 +1853,23 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
     return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
 }
 
+void llm_graph_context::build_dense_out(
+    ggml_tensor * dense_2,
+    ggml_tensor * dense_3) const {
+    if (!cparams.embeddings || dense_2 == nullptr || dense_3 == nullptr) {
+        return;
+    }
+    ggml_tensor * cur = res->t_embd_pooled != nullptr ? res->t_embd_pooled : res->t_embd;
+    GGML_ASSERT(cur != nullptr && "missing t_embd_pooled/t_embd");
+
+    cur = ggml_mul_mat(ctx0, dense_2, cur);
+    cur = ggml_mul_mat(ctx0, dense_3, cur);
+    cb(cur, "result_embd_pooled", -1);
+    res->t_embd_pooled = cur;
+    ggml_build_forward_expand(gf, cur);
+}
+
+
 void llm_graph_context::build_pooling(
         ggml_tensor * cls,
         ggml_tensor * cls_b,

diff --git a/examples/talk-llama/llama-graph.h b/examples/talk-llama/llama-graph.h
index 34b984afeb04379e1e7b5aa4c792931fd3b03b9e..dc84b7942893a10d4084950c70d529ebcd051c67 100644 (file)
--- a/examples/talk-llama/llama-graph.h
+++ b/examples/talk-llama/llama-graph.h
@@ -814,6 +814,14 @@ struct llm_graph_context {
             ggml_tensor * cls_b,
             ggml_tensor * cls_out,
             ggml_tensor * cls_out_b) const;
+
+    //
+    // dense (out)
+    //
+
+    void build_dense_out(
+            ggml_tensor * dense_2,
+            ggml_tensor * dense_3) const;
 };
 
 // TODO: better name

diff --git a/examples/talk-llama/llama-hparams.cpp b/examples/talk-llama/llama-hparams.cpp
index c04ac58f1af4ba3746c97ddefcfd723c390ad027..db65d69eabdcbd3dcc106428b90dd72afb5b04d3 100644 (file)
--- a/examples/talk-llama/llama-hparams.cpp
+++ b/examples/talk-llama/llama-hparams.cpp
@@ -140,7 +140,11 @@ uint32_t llama_hparams::n_embd_s() const {
 }
 
 bool llama_hparams::is_recurrent(uint32_t il) const {
-    return recurrent_layer_arr[il];
+    if (il < n_layer) {
+        return recurrent_layer_arr[il];
+    }
+
+    GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer);
 }
 
 uint32_t llama_hparams::n_pos_per_embd() const {

diff --git a/examples/talk-llama/llama-hparams.h b/examples/talk-llama/llama-hparams.h
index 0fe4b569424056425ad79b9307afec868ea24706..4e7f73ec234c33f1164b2e191273436f17bda2fd 100644 (file)
--- a/examples/talk-llama/llama-hparams.h
+++ b/examples/talk-llama/llama-hparams.h
@@ -42,7 +42,7 @@ struct llama_hparams {
     uint32_t n_embd;
     uint32_t n_embd_features = 0;
     uint32_t n_layer;
-     int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
+    int32_t n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache
     uint32_t n_rot;
     uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
     uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
@@ -169,6 +169,18 @@ struct llama_hparams {
     uint32_t laurel_rank  = 64;
     uint32_t n_embd_altup = 256;
 
+    // needed for sentence-transformers dense layers
+    uint32_t dense_2_feat_in  = 0;  // in_features of the 2_Dense
+    uint32_t dense_2_feat_out = 0;  // out_features of the 2_Dense
+    uint32_t dense_3_feat_in  = 0;  // in_features of the 3_Dense
+    uint32_t dense_3_feat_out = 0;  // out_features of the 3_Dense
+
+    // xIELU
+    std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_n;
+    std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_p;
+    std::array<float, LLAMA_MAX_LAYERS> xielu_beta;
+    std::array<float, LLAMA_MAX_LAYERS> xielu_eps;
+
     // needed by encoder-decoder models (e.g. T5, FLAN-T5)
     // ref: https://github.com/ggerganov/llama.cpp/pull/8141
     llama_token dec_start_token_id = LLAMA_TOKEN_NULL;

diff --git a/examples/talk-llama/llama-kv-cache-iswa.cpp b/examples/talk-llama/llama-kv-cache-iswa.cpp
index 827302e6d25bd486562af65c345b21cbeae6ff5a..facba1d004012b503ebe22f35f022a4d5dfe1a8f 100644 (file)
--- a/examples/talk-llama/llama-kv-cache-iswa.cpp
+++ b/examples/talk-llama/llama-kv-cache-iswa.cpp
@@ -220,7 +220,7 @@ bool llama_kv_cache_iswa::get_can_shift() const {
 }
 
 void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
-    if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
         kv_base->state_write(io, seq_id, flags);
     }
 
@@ -228,7 +228,7 @@ void llama_kv_cache_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id
 }
 
 void llama_kv_cache_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
-    if ((flags & LLAMA_STATE_SEQ_FLAGS_SWA_ONLY) == 0) {
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
         kv_base->state_read(io, seq_id, flags);
     }
 

diff --git a/examples/talk-llama/llama-kv-cache.cpp b/examples/talk-llama/llama-kv-cache.cpp
index 816f2d5de592b9949cfab53f071e0acae80ace20..736693e174527d76aeaabd7f27855d72e033ae66 100644 (file)
--- a/examples/talk-llama/llama-kv-cache.cpp
+++ b/examples/talk-llama/llama-kv-cache.cpp
@@ -123,11 +123,8 @@ llama_kv_cache::llama_kv_cache(
             throw std::runtime_error("failed to create ggml context for kv cache");
         }
 
-        ggml_tensor * k;
-        ggml_tensor * v;
-
-        k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream);
-        v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream);
+        ggml_tensor * k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream);
+        ggml_tensor * v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream);
 
         ggml_format_name(k, "cache_k_l%d", il);
         ggml_format_name(v, "cache_v_l%d", il);

diff --git a/examples/talk-llama/llama-memory-hybrid.cpp b/examples/talk-llama/llama-memory-hybrid.cpp
index abf652483c202971a9e3a36d0d200313bf59bc49..dfb8439e01bdfb53fd2ac2a270d6534a7fac7a60 100644 (file)
--- a/examples/talk-llama/llama-memory-hybrid.cpp
+++ b/examples/talk-llama/llama-memory-hybrid.cpp
@@ -73,7 +73,9 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                ubatch = balloc.split_equal(n_ubatch, false);
+                // TODO: non-sequential equal split can be done if using unified KV cache
+                //       for simplicity, we always use sequential equal split for now
+                ubatch = balloc.split_equal(n_ubatch, true);
             }
 
             if (ubatch.n_tokens == 0) {
@@ -175,17 +177,17 @@ std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid::memory_breakdo
 }
 
 void llama_memory_hybrid::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
-    GGML_UNUSED(flags);
-
-    mem_attn->state_write(io, seq_id);
-    mem_recr->state_write(io, seq_id);
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
+        mem_attn->state_write(io, seq_id, flags);
+    }
+    mem_recr->state_write(io, seq_id, flags);
 }
 
 void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
-    GGML_UNUSED(flags);
-
-    mem_attn->state_read(io, seq_id);
-    mem_recr->state_read(io, seq_id);
+    if ((flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY) == 0) {
+        mem_attn->state_read(io, seq_id, flags);
+    }
+    mem_recr->state_read(io, seq_id, flags);
 }
 
 llama_kv_cache * llama_memory_hybrid::get_mem_attn() const {

diff --git a/examples/talk-llama/llama-memory-recurrent.cpp b/examples/talk-llama/llama-memory-recurrent.cpp
index 44645fcdd2d4824edd704e02c57d716db84cbd4d..d67f5a5f47b87c2440a2a20c281ac71692498e81 100644 (file)
--- a/examples/talk-llama/llama-memory-recurrent.cpp
+++ b/examples/talk-llama/llama-memory-recurrent.cpp
@@ -136,6 +136,7 @@ void llama_memory_recurrent::clear(bool data) {
 }
 
 bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    //printf("[DEBUG] calling llama_memory_recurrent::seq_rm` with `seq_id=%d, p0=%d, p1=%d`\n", seq_id, p0, p1);
     uint32_t new_head = size;
 
     if (p0 < 0) {
@@ -156,7 +157,8 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
         if (tail_id >= 0) {
             const auto & cell = cells[tail_id];
             // partial intersection is invalid
-            if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) {
+            if ((0 < p0 && p0 < cell.pos) || (0 < p1 && p1 <= cell.pos)) {
+                //printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: partial intersection is invalid, so returning false\n");
                 return false;
             }
             // invalidate tails which will be cleared
@@ -167,6 +169,7 @@ bool llama_memory_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
     } else {
         // seq_id is negative, then the range should include everything or nothing
         if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
+            //printf("[DEBUG] inside `llama_memory_recurrent::seq_rm`: `seq_id` is negative, so returning false\n");
             return false;
         }
     }
@@ -379,7 +382,9 @@ llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr &
                 // if all tokens are output, split by sequence
                 ubatch = balloc.split_seq(n_ubatch);
             } else {
-                ubatch = balloc.split_equal(n_ubatch, false);
+                // TODO: non-sequential equal split can be done if using unified KV cache
+                //       for simplicity, we always use sequential equal split for now
+                ubatch = balloc.split_equal(n_ubatch, true);
             }
 
             if (ubatch.n_tokens == 0) {
@@ -856,9 +861,12 @@ void llama_memory_recurrent::state_write_data(llama_io_write_i & io, const std::
 bool llama_memory_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) {
     if (dest_seq_id != -1) {
         // single sequence
-
         seq_rm(dest_seq_id, -1, -1);
 
+        if (cell_count == 0) {
+            return true;
+        }
+
         llama_batch_allocr balloc(hparams.n_pos_per_embd());
 
         llama_ubatch ubatch = balloc.ubatch_reserve(cell_count, 1);

diff --git a/examples/talk-llama/llama-model-loader.cpp b/examples/talk-llama/llama-model-loader.cpp
index 8182a9adf53a63e119a55d1cff3c321b0fece578..aa3a65f87a5420d040b1ab42fe1389a5e7c8d412 100644 (file)
--- a/examples/talk-llama/llama-model-loader.cpp
+++ b/examples/talk-llama/llama-model-loader.cpp
@@ -465,6 +465,8 @@ namespace GGUFMeta {
     // TODO: this is not very clever - figure out something better
     template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
     template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
+    template bool llama_model_loader::get_key_or_arr<std::array<float, 512>>(enum llm_kv kid, std::array<float, 512> & result, uint32_t n, bool required);
+
 
 llama_model_loader::llama_model_loader(
         const std::string & fname,

diff --git a/examples/talk-llama/llama-model.cpp b/examples/talk-llama/llama-model.cpp
index ffd9286ef8912f9609ca82d20bac483d5e5fde8d..36d495d6cfeab7ccb5bbb76922acd7f33e837486 100644 (file)
--- a/examples/talk-llama/llama-model.cpp
+++ b/examples/talk-llama/llama-model.cpp
@@ -114,6 +114,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_17B_16E:       return "17Bx16E (Scout)";
         case LLM_TYPE_17B_128E:      return "17Bx128E (Maverick)";
         case LLM_TYPE_A13B:          return "A13B";
+        case LLM_TYPE_8B_A1B:        return "8B.A1B";
         case LLM_TYPE_21B_A3B:       return "21B.A3B";
         case LLM_TYPE_30B_A3B:       return "30B.A3B";
         case LLM_TYPE_106B_A12B:     return "106B.A12B";
@@ -310,7 +311,7 @@ static ggml_backend_buffer_type_t select_weight_buft(const llama_hparams & hpara
 }
 
 // CPU: ACCEL -> GPU host -> CPU extra -> CPU
-static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices, bool use_extra_bufts) {
+static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices, bool use_extra_bufts, bool no_host) {
     buft_list_t buft_list;
 
     // add ACCEL buffer types
@@ -331,11 +332,13 @@ static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & de
     // generally, this will be done using the first device in the list
     // a better approach would be to handle this on a weight-by-weight basis using the offload_op
     // function of the device to determine if it would benefit from being stored in a host buffer
-    for (auto * dev : devices) {
-        ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
-        if (buft) {
-            buft_list.emplace_back(dev, buft);
-            break;
+    if (!no_host) {
+        for (auto * dev : devices) {
+            ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
+            if (buft) {
+                buft_list.emplace_back(dev, buft);
+                break;
+            }
         }
     }
 
@@ -512,9 +515,13 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         llm_arch_is_recurrent(ml.get_arch()));
 
     std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0);
-
     std::fill(hparams.swa_layers.begin(), hparams.swa_layers.end(), 0);
 
+    std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f);
+    std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
+    std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
+    std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
+
     ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer, false);
     ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
 
@@ -1084,7 +1091,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                         }
                         break;
                     default: type = LLM_TYPE_UNKNOWN;
-               }
+                }
+
+                // Load attention parameters
+                ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH,   hparams.n_embd_head_k, false);
+                ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
             } break;
         case LLM_ARCH_GPT2:
             {
@@ -1207,12 +1218,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 hparams.set_swa_pattern(6);
 
                 hparams.causal_attn = false; // embeddings do not use causal attention
-                hparams.rope_freq_base_train_swa  = 10000.0f;
+                hparams.rope_freq_base_train_swa = 10000.0f;
                 hparams.rope_freq_scale_train_swa = 1.0f;
 
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+                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_POOLING_TYPE,                hparams.pooling_type);
+                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
+
+                //applied only if model converted with --sentence-transformers-dense-modules
+                ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
+                ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
+                ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
+                ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
+
+                GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
+                GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
 
                 switch (hparams.n_layer) {
                     case 24: type = LLM_TYPE_0_3B; break;
@@ -1985,13 +2005,28 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 for (uint32_t il = 0; il < hparams.n_layer; ++il) {
                     hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
                 }
+                hparams.n_layer_dense_lead = hparams.n_layer;
                 switch (hparams.n_ff()) {
                     case  4608: type = LLM_TYPE_350M; break;
                     case  6912: type = LLM_TYPE_700M; break;
                     case  8192: type = LLM_TYPE_1_2B; break;
                     case 10752: type = LLM_TYPE_2_6B; break;
-                    default:   type = LLM_TYPE_UNKNOWN;
+                    default:    type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_LFM2MOE:
+            {
+                ml.get_key(LLM_KV_SHORTCONV_L_CACHE,           hparams.n_shortconv_l_cache);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func);
+
+                for (uint32_t il = 0; il < hparams.n_layer; ++il) {
+                    hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
                 }
+
+                type = LLM_TYPE_8B_A1B;
             } break;
         case LLM_ARCH_SMALLTHINKER:
             {
@@ -2029,6 +2064,19 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_APERTUS:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_N,        hparams.xielu_alpha_n, hparams.n_layer);
+                ml.get_key_or_arr(LLM_KV_XIELU_ALPHA_P,        hparams.xielu_alpha_p, hparams.n_layer);
+                ml.get_key_or_arr(LLM_KV_XIELU_BETA,           hparams.xielu_beta,    hparams.n_layer);
+                ml.get_key_or_arr(LLM_KV_XIELU_EPS,            hparams.xielu_eps,     hparams.n_layer);
+
+                switch (hparams.n_layer) {
+                    case 32: type = LLM_TYPE_8B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -2062,7 +2110,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
     LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s)\n", __func__, ml.use_mmap ? "true" : "false");
 
     // build a list of buffer types for the CPU and GPU devices
-    pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts);
+    pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
     for (auto * dev : devices) {
         buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split);
         // add CPU buffer types as a fallback
@@ -3392,17 +3440,17 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 } break;
             case LLM_ARCH_PLAMO2:
                 {
+                    // mamba parameters
                     const uint32_t d_conv             = hparams.ssm_d_conv;
                     const uint32_t d_state            = hparams.ssm_d_state;
                     const uint32_t num_heads          = hparams.ssm_dt_rank;
                     const uint32_t intermediate_size  = hparams.ssm_d_inner;
-                    const uint32_t head_dim           = intermediate_size / num_heads;
-                    const uint32_t qk_dim             = head_dim;
-                    const uint32_t v_dim              = head_dim;
-                    const int64_t num_attention_heads = hparams.n_head();
-                    const int64_t q_num_heads         = num_attention_heads;
                     const int64_t dt_dim              = std::max(64, int(hparams.n_embd / 16));
 
+                    // attention parameters
+                    const uint32_t qk_dim = hparams.n_embd_head_k;
+                    const uint32_t v_dim  = hparams.n_embd_head_v;
+
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
                     // output
@@ -3436,6 +3484,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                             layer.ssm_b_norm = create_tensor(tn(LLM_TENSOR_SSM_B_NORM, i), {d_state}, 0);
                             layer.ssm_c_norm = create_tensor(tn(LLM_TENSOR_SSM_C_NORM, i), {d_state}, 0);
                         } else {
+                            const int64_t num_attention_heads = hparams.n_head(i);
+                            const int64_t q_num_heads         = num_attention_heads;
                             const int64_t num_key_value_heads = hparams.n_head_kv(i);
                             const int64_t k_num_heads         = num_key_value_heads;
                             const int64_t v_num_heads         = num_key_value_heads;
@@ -3444,8 +3494,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                             const int64_t v_proj_dim          = v_num_heads * v_dim;
 
                             layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, q_proj_dim + k_proj_dim + v_proj_dim}, 0);
-                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim, num_attention_heads}, 0);
-                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim, k_num_heads}, 0);
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {qk_dim, num_attention_heads}, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {qk_dim, k_num_heads}, 0);
                             layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {q_num_heads * v_dim, n_embd}, 0);
                         }
 
@@ -3645,6 +3695,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
                     }
 
+                    // Dense linear weights
+                    dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
+                    dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
+
+
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
 
@@ -4825,11 +4880,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers
                         if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
                             layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
-                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags);
                             layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
                             layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
-                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags);
-                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags);
+
+                            // Optional tensors
+                            layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+                            layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, flags | TENSOR_NOT_REQUIRED);
+                            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags | TENSOR_NOT_REQUIRED);
                         }
                     }
                 }
@@ -5787,6 +5844,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     }
                 } break;
             case LLM_ARCH_LFM2:
+            case LLM_ARCH_LFM2MOE:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
                     tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
@@ -5798,11 +5856,23 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];
-                        // ffn is same for transformer and conv layers
+
+                        const bool is_moe_layer = i >= static_cast<int>(hparams.n_layer_dense_lead);
+
+                        // ffn/moe is same for transformer and conv layers
                         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);
+                        if (is_moe_layer) {
+                            GGML_ASSERT(n_expert && n_expert_used);
+                            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i),  {n_embd, n_expert}, 0);
+                            layer.ffn_gate_exps   = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp,   n_embd, n_expert}, 0);
+                            layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i),   {n_embd, hparams.n_ff_exp, n_expert}, 0);
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+                        } else {  // dense
+                            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);
+                        }
 
                         // for operator_norm
                         layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
@@ -5907,6 +5977,48 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_up_chexps   = create_tensor(tn(LLM_TENSOR_FFN_UP_CHEXPS,   "weight", i), {  n_embd, n_ff_chexp, n_chunk_expert}, 0);
                     }
                 } break;
+            case LLM_ARCH_APERTUS:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    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 }, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
+                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        } else {
+                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+                        }
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                        // optional bias tensors
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), { n_embd },     TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), { n_embd_gqa }, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), { n_embd_gqa }, TENSOR_NOT_REQUIRED);
+                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd },     TENSOR_NOT_REQUIRED);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 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);
+
+                        // Q and K layernorms for Apertus
+                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -6241,7 +6353,7 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: expert_weights_norm  = %d\n",     __func__, hparams.expert_weights_norm);
     }
 
-    if (arch == LLM_ARCH_SMALLTHINKER) {
+    if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
         LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
         LLAMA_LOG_INFO("%s: expert_gating_func   = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
     }
@@ -7776,6 +7888,8 @@ struct llm_build_bert : public llm_graph_context {
                 }
 
                 if (model.layers[il].attn_q_norm) {
+                    Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head*n_head, n_tokens);
+
                     Qcur = build_norm(Qcur,
                             model.layers[il].attn_q_norm,
                             model.layers[il].attn_q_norm_b,
@@ -7785,6 +7899,8 @@ struct llm_build_bert : public llm_graph_context {
                 }
 
                 if (model.layers[il].attn_k_norm) {
+                    Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head*n_head_kv, n_tokens);
+
                     Kcur = build_norm(Kcur,
                             model.layers[il].attn_k_norm,
                             model.layers[il].attn_k_norm_b,
@@ -8167,6 +8283,9 @@ struct llm_build_mpt : public llm_graph_context {
 
                 // Q/K Layernorm
                 if (model.layers[il].attn_q_norm) {
+                    Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head*n_head,    n_tokens);
+                    Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head*n_head_kv, n_tokens);
+
                     Qcur = build_norm(Qcur,
                             model.layers[il].attn_q_norm,
                             model.layers[il].attn_q_norm_b,
@@ -11751,6 +11870,7 @@ struct llm_graph_context_mamba : public llm_graph_context {
             // TODO: skip computing output earlier for unused tokens
 
             y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
+            cb(y, "mamba2_y_add_d", il);
             y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
 
             // grouped RMS norm
@@ -14705,6 +14825,7 @@ struct llm_build_nemotron_h : public llm_graph_context_mamba {
         ggml_tensor * inpL;
 
         inpL = build_inp_embd(model.tok_embd);
+        ggml_build_forward_expand(gf, inpL);
 
         auto * inp = build_inp_mem_hybrid();
 
@@ -14736,7 +14857,7 @@ struct llm_build_nemotron_h : public llm_graph_context_mamba {
 
             // add residual
             cur = ggml_add(ctx0, cur, inpSA);
-            cb(cur, "block_out", il);
+            cb(cur, "nemotron_h_block_out", il);
 
             // input for next layer
             inpL = cur;
@@ -16192,10 +16313,10 @@ struct llm_build_granite_hybrid : public llm_graph_context_mamba {
     }
 
     ggml_tensor * build_layer_ffn(
-              ggml_tensor       * cur,
-              ggml_tensor       * inpSA,
-        const llama_model       & model,
-        const int                 il) {
+              ggml_tensor * cur,
+              ggml_tensor * inpSA,
+        const llama_model & model,
+        const int           il) {
 
         // For Granite architectures - scale residual
         if (hparams.f_residual_scale) {
@@ -17607,6 +17728,7 @@ private:
             const int64_t n_embd_head_q = hparams.n_embd_head_k;
             const int64_t n_embd_head_k = hparams.n_embd_head_k;
             const int64_t n_embd_head_v = hparams.n_embd_head_v;
+            int32_t n_head = hparams.n_head(il);
             int32_t n_head_kv = hparams.n_head_kv(il);
 
             const int64_t q_offset = 0;
@@ -18523,6 +18645,8 @@ struct llm_build_lfm2 : public llm_graph_context {
         ggml_tensor * inp_out_ids = build_inp_out_ids();
 
         for (int il = 0; il < n_layer; ++il) {
+            const bool is_moe_layer = il >= static_cast<int>(hparams.n_layer_dense_lead);
+
             auto * prev_cur = cur;
             cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
             cb(cur, "model.layers.{}.operator_norm", il);
@@ -18537,7 +18661,16 @@ struct llm_build_lfm2 : public llm_graph_context {
             }
 
             cur = ggml_add(ctx0, prev_cur, cur);
-            cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
+
+            auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(ffn_norm_out, "model.layers.{}.ffn_norm", il);
+
+            ggml_tensor * ffn_out = is_moe_layer ?
+                build_moe_feed_forward(ffn_norm_out, il) :
+                build_dense_feed_forward(ffn_norm_out, il);
+            cb(ffn_norm_out, "model.layers.{}.ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_out);
         }
 
         cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
@@ -18552,23 +18685,32 @@ struct llm_build_lfm2 : public llm_graph_context {
         ggml_build_forward_expand(gf, cur);
     }
 
-    ggml_tensor * build_feed_forward(ggml_tensor * cur,
-                                     int           il) const {
-        cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-        cb(cur, "model.layers.{}.ffn_norm", il);
+    ggml_tensor * build_moe_feed_forward(ggml_tensor * cur,
+                                         int           il) const {
+        return 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, true,
+                    false, 0.0,
+                    static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
+                    il);
+    }
 
+    ggml_tensor * build_dense_feed_forward(ggml_tensor * cur,
+                                           int           il) const {
         GGML_ASSERT(!model.layers[il].ffn_up_b);
         GGML_ASSERT(!model.layers[il].ffn_gate_b);
         GGML_ASSERT(!model.layers[il].ffn_down_b);
-        cur = build_ffn(cur,
+        return build_ffn(cur,
                 model.layers[il].ffn_up,   NULL, NULL,
                 model.layers[il].ffn_gate, NULL, NULL,
                 model.layers[il].ffn_down, NULL, NULL,
                 NULL,
                 LLM_FFN_SILU, LLM_FFN_PAR, il);
-        cb(cur, "model.layers.{}.feed_forward.w2", il);
-
-        return cur;
     }
 
     ggml_tensor * build_attn_block(ggml_tensor             * cur,
@@ -19088,6 +19230,141 @@ struct llm_build_grovemoe : public llm_graph_context {
     }
 };
 
+struct llm_build_apertus : public llm_graph_context {
+    llm_build_apertus(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(n_embd_head == hparams.n_rot);
+
+        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, nullptr,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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_pos", il);
+                cb(Kcur, "Kcur_pos", il);
+                cb(Vcur, "Vcur_pos", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        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);
+
+            // feed-forward network with xIELU activation
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, nullptr,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                // Up projection
+                ggml_tensor * up = build_lora_mm(model.layers[il].ffn_up, cur);
+                cb(up, "ffn_up", il);
+
+                float alpha_n_val = hparams.xielu_alpha_n[il];
+                float alpha_p_val = hparams.xielu_alpha_p[il];
+                float beta_val = hparams.xielu_beta[il];
+                float eps_val = hparams.xielu_eps[il];
+
+                // Apply xIELU activation
+                ggml_tensor * activated = ggml_xielu(ctx0, up, alpha_n_val, alpha_p_val, beta_val, eps_val);
+                cb(activated, "ffn_xielu", il);
+
+                // Down projection
+                cur = build_lora_mm(model.layers[il].ffn_down, activated);
+                cb(cur, "ffn_down", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", 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);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
     llama_memory_i * res;
 
@@ -19603,6 +19880,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
                 llm = std::make_unique<llm_build_falcon_h1>(*this, params);
             } break;
         case LLM_ARCH_LFM2:
+        case LLM_ARCH_LFM2MOE:
             {
                 llm = std::make_unique<llm_build_lfm2>(*this, params);
             } break;
@@ -19618,6 +19896,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_grovemoe>(*this, params);
             } break;
+        case LLM_ARCH_APERTUS:
+            {
+                llm = std::make_unique<llm_build_apertus>(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -19625,6 +19907,12 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
     // add on pooling layer
     llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
 
+    // if the gguf model was converted with --sentence-transformers-dense-modules
+    // there will be two additional dense projection layers
+    // dense linear projections are applied after pooling
+    // TODO: move reranking logic here and generalize
+    llm->build_dense_out(dense_2_out_layers, dense_3_out_layers);
+
     return llm->res->get_gf();
 }
 
@@ -19649,6 +19937,7 @@ llama_model_params llama_model_default_params() {
         /*.use_mlock                   =*/ false,
         /*.check_tensors               =*/ false,
         /*.use_extra_bufts             =*/ true,
+        /*.no_host                     =*/ false,
     };
 
     return result;
@@ -19820,10 +20109,12 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_OPENAI_MOE:
         case LLM_ARCH_HUNYUAN_DENSE:
         case LLM_ARCH_LFM2:
+        case LLM_ARCH_LFM2MOE:
         case LLM_ARCH_SMALLTHINKER:
         case LLM_ARCH_GLM4_MOE:
         case LLM_ARCH_SEED_OSS:
         case LLM_ARCH_GROVEMOE:
+        case LLM_ARCH_APERTUS:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:
@@ -19934,6 +20225,10 @@ bool llama_model_is_recurrent(const llama_model * model) {
     return llm_arch_is_recurrent(model->arch);
 }
 
+bool llama_model_is_hybrid(const llama_model * model) {
+    return llm_arch_is_hybrid(model->arch);
+}
+
 bool llama_model_is_diffusion(const llama_model * model) {
     return llm_arch_is_diffusion(model->arch);
 }

diff --git a/examples/talk-llama/llama-model.h b/examples/talk-llama/llama-model.h
index d73ce9693230f8662ac42a3ff9e6bfa369afc9cb..7f48662f2807ac46e6b24335e850e3585b1575ed 100644 (file)
--- a/examples/talk-llama/llama-model.h
+++ b/examples/talk-llama/llama-model.h
@@ -107,6 +107,7 @@ enum llm_type {
     LLM_TYPE_17B_16E, // llama4 Scout
     LLM_TYPE_17B_128E, // llama4 Maverick
     LLM_TYPE_A13B,
+    LLM_TYPE_8B_A1B, // lfm2moe
     LLM_TYPE_21B_A3B, // Ernie MoE small
     LLM_TYPE_30B_A3B,
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
@@ -380,6 +381,12 @@ struct llama_layer {
     // openai-moe
     struct ggml_tensor * attn_sinks = nullptr;
 
+    // xIELU activation parameters for Apertus
+    struct ggml_tensor * ffn_act_alpha_n = nullptr;
+    struct ggml_tensor * ffn_act_alpha_p = nullptr;
+    struct ggml_tensor * ffn_act_beta    = nullptr;
+    struct ggml_tensor * ffn_act_eps     = nullptr;
+
     struct llama_layer_posnet posnet;
 
     struct llama_layer_convnext convnext;
@@ -431,6 +438,12 @@ struct llama_model {
 
     std::vector<llama_layer> layers;
 
+    //Dense linear projections for SentenceTransformers models like embeddinggemma
+    // For Sentence Transformers models structure see
+    // https://sbert.net/docs/sentence_transformer/usage/custom_models.html#structure-of-sentence-transformer-models
+    struct ggml_tensor * dense_2_out_layers = nullptr;
+    struct ggml_tensor * dense_3_out_layers = nullptr;
+
     llama_model_params params;
 
     // gguf metadata

diff --git a/examples/talk-llama/llama-sampling.cpp b/examples/talk-llama/llama-sampling.cpp
index 2186f827bf54307731d1fbb57e9b38b380415f94..55d2e355fd8bb50f4257ad652add533856390298 100644 (file)
--- a/examples/talk-llama/llama-sampling.cpp
+++ b/examples/talk-llama/llama-sampling.cpp
@@ -2541,8 +2541,13 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
     if (n_non_eog == 0) {
         cur_p->size = 1;
         cur_p->data[0].id = ctx->vocab->token_eot();
+        if (cur_p->data[0].id == LLAMA_TOKEN_NULL) {
+            cur_p->data[0].id = ctx->vocab->token_eos();
+        }
         cur_p->data[0].logit = 1.0f;
 
+        GGML_ASSERT(cur_p->data[0].id != LLAMA_TOKEN_NULL);
+
         return;
     }
 

diff --git a/examples/talk-llama/llama-vocab.cpp b/examples/talk-llama/llama-vocab.cpp
index da938af03bf080828e23e2aaa1dc4dbb3fd64158..7fffd171491aa31f8f063c8a8daba946f72ac4f8 100644 (file)
--- a/examples/talk-llama/llama-vocab.cpp
+++ b/examples/talk-llama/llama-vocab.cpp
@@ -347,6 +347,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
             case LLAMA_VOCAB_PRE_TYPE_OLMO:
             case LLAMA_VOCAB_PRE_TYPE_JAIS:
             case LLAMA_VOCAB_PRE_TYPE_TRILLION:
+            case LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING:
                 regex_exprs = {
                     "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
                 };
@@ -1961,6 +1962,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "trillion") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_TRILLION;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "granite-docling") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING;
+                clean_spaces = false;
             } else if (
                 tokenizer_pre == "bailingmoe" ||
                 tokenizer_pre == "llada-moe") {
@@ -2166,6 +2171,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|end|>"
                         || t.first == "<end_of_turn>"
                         || t.first == "<|endoftext|>"
+                        || t.first == "<|end_of_text|>" // granite
                         || t.first == "<EOT>"
                         || t.first == "_<EOT>"
                         || t.first == "<｜end▁of▁sentence｜>" // DeepSeek

diff --git a/examples/talk-llama/llama-vocab.h b/examples/talk-llama/llama-vocab.h
index 0d2f28c36c80dd714a6a2d01981872583dbbc7cd..5e468675e4447b959c6369d411bc1d876b6c6fd5 100644 (file)
--- a/examples/talk-llama/llama-vocab.h
+++ b/examples/talk-llama/llama-vocab.h
@@ -8,46 +8,47 @@
 
 // pre-tokenization types
 enum llama_vocab_pre_type {
-    LLAMA_VOCAB_PRE_TYPE_DEFAULT        = 0,
-    LLAMA_VOCAB_PRE_TYPE_LLAMA3         = 1,
-    LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM   = 2,
-    LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
-    LLAMA_VOCAB_PRE_TYPE_FALCON         = 4,
-    LLAMA_VOCAB_PRE_TYPE_MPT            = 5,
-    LLAMA_VOCAB_PRE_TYPE_STARCODER      = 6,
-    LLAMA_VOCAB_PRE_TYPE_GPT2           = 7,
-    LLAMA_VOCAB_PRE_TYPE_REFACT         = 8,
-    LLAMA_VOCAB_PRE_TYPE_COMMAND_R      = 9,
-    LLAMA_VOCAB_PRE_TYPE_STABLELM2      = 10,
-    LLAMA_VOCAB_PRE_TYPE_QWEN2          = 11,
-    LLAMA_VOCAB_PRE_TYPE_OLMO           = 12,
-    LLAMA_VOCAB_PRE_TYPE_DBRX           = 13,
-    LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
-    LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
-    LLAMA_VOCAB_PRE_TYPE_CHATGLM3       = 16,
-    LLAMA_VOCAB_PRE_TYPE_CHATGLM4       = 17,
-    LLAMA_VOCAB_PRE_TYPE_VIKING         = 18,
-    LLAMA_VOCAB_PRE_TYPE_JAIS           = 19,
-    LLAMA_VOCAB_PRE_TYPE_TEKKEN         = 20,
-    LLAMA_VOCAB_PRE_TYPE_SMOLLM         = 21,
-    LLAMA_VOCAB_PRE_TYPE_CODESHELL      = 22,
-    LLAMA_VOCAB_PRE_TYPE_BLOOM          = 23,
-    LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH   = 24,
-    LLAMA_VOCAB_PRE_TYPE_EXAONE         = 25,
-    LLAMA_VOCAB_PRE_TYPE_CHAMELEON      = 26,
-    LLAMA_VOCAB_PRE_TYPE_MINERVA        = 27,
-    LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM  = 28,
-    LLAMA_VOCAB_PRE_TYPE_GPT4O          = 29,
-    LLAMA_VOCAB_PRE_TYPE_SUPERBPE       = 30,
-    LLAMA_VOCAB_PRE_TYPE_TRILLION       = 31,
-    LLAMA_VOCAB_PRE_TYPE_BAILINGMOE     = 32,
-    LLAMA_VOCAB_PRE_TYPE_LLAMA4         = 33,
-    LLAMA_VOCAB_PRE_TYPE_PIXTRAL        = 34,
-    LLAMA_VOCAB_PRE_TYPE_SEED_CODER     = 35,
-    LLAMA_VOCAB_PRE_TYPE_HUNYUAN        = 36,
-    LLAMA_VOCAB_PRE_TYPE_KIMI_K2        = 37,
-    LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE  = 38,
-    LLAMA_VOCAB_PRE_TYPE_GROK_2         = 39,
+    LLAMA_VOCAB_PRE_TYPE_DEFAULT         = 0,
+    LLAMA_VOCAB_PRE_TYPE_LLAMA3          = 1,
+    LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM    = 2,
+    LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER  = 3,
+    LLAMA_VOCAB_PRE_TYPE_FALCON          = 4,
+    LLAMA_VOCAB_PRE_TYPE_MPT             = 5,
+    LLAMA_VOCAB_PRE_TYPE_STARCODER       = 6,
+    LLAMA_VOCAB_PRE_TYPE_GPT2            = 7,
+    LLAMA_VOCAB_PRE_TYPE_REFACT          = 8,
+    LLAMA_VOCAB_PRE_TYPE_COMMAND_R       = 9,
+    LLAMA_VOCAB_PRE_TYPE_STABLELM2       = 10,
+    LLAMA_VOCAB_PRE_TYPE_QWEN2           = 11,
+    LLAMA_VOCAB_PRE_TYPE_OLMO            = 12,
+    LLAMA_VOCAB_PRE_TYPE_DBRX            = 13,
+    LLAMA_VOCAB_PRE_TYPE_SMAUG           = 14,
+    LLAMA_VOCAB_PRE_TYPE_PORO            = 15,
+    LLAMA_VOCAB_PRE_TYPE_CHATGLM3        = 16,
+    LLAMA_VOCAB_PRE_TYPE_CHATGLM4        = 17,
+    LLAMA_VOCAB_PRE_TYPE_VIKING          = 18,
+    LLAMA_VOCAB_PRE_TYPE_JAIS            = 19,
+    LLAMA_VOCAB_PRE_TYPE_TEKKEN          = 20,
+    LLAMA_VOCAB_PRE_TYPE_SMOLLM          = 21,
+    LLAMA_VOCAB_PRE_TYPE_CODESHELL       = 22,
+    LLAMA_VOCAB_PRE_TYPE_BLOOM           = 23,
+    LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH    = 24,
+    LLAMA_VOCAB_PRE_TYPE_EXAONE          = 25,
+    LLAMA_VOCAB_PRE_TYPE_CHAMELEON       = 26,
+    LLAMA_VOCAB_PRE_TYPE_MINERVA         = 27,
+    LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM   = 28,
+    LLAMA_VOCAB_PRE_TYPE_GPT4O           = 29,
+    LLAMA_VOCAB_PRE_TYPE_SUPERBPE        = 30,
+    LLAMA_VOCAB_PRE_TYPE_TRILLION        = 31,
+    LLAMA_VOCAB_PRE_TYPE_BAILINGMOE      = 32,
+    LLAMA_VOCAB_PRE_TYPE_LLAMA4          = 33,
+    LLAMA_VOCAB_PRE_TYPE_PIXTRAL         = 34,
+    LLAMA_VOCAB_PRE_TYPE_SEED_CODER      = 35,
+    LLAMA_VOCAB_PRE_TYPE_HUNYUAN         = 36,
+    LLAMA_VOCAB_PRE_TYPE_KIMI_K2         = 37,
+    LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE   = 38,
+    LLAMA_VOCAB_PRE_TYPE_GROK_2          = 39,
+    LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
 };
 
 struct LLM_KV;

diff --git a/examples/talk-llama/llama.h b/examples/talk-llama/llama.h
index 452d9ec5bf285425b8935bf209a6262dbd0cbade..a0a660bff88dae73307bdd3e88d44980233b7ae2 100644 (file)
--- a/examples/talk-llama/llama.h
+++ b/examples/talk-llama/llama.h
@@ -296,6 +296,7 @@ extern "C" {
         bool use_mlock;       // force system to keep model in RAM
         bool check_tensors;   // validate model tensor data
         bool use_extra_bufts; // use extra buffer types (used for weight repacking)
+        bool no_host;         // bypass host buffer allowing extra buffers to be used
     };
 
     // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
@@ -543,6 +544,9 @@ extern "C" {
     // Returns true if the model is recurrent (like Mamba, RWKV, etc.)
     LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model);
 
+    // Returns true if the model is hybrid (like Jamba, Granite, etc.)
+    LLAMA_API bool llama_model_is_hybrid(const struct llama_model * model);
+
     // Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)
     LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model);
 
@@ -791,8 +795,12 @@ extern "C" {
                           size_t   n_token_capacity,
                           size_t * n_token_count_out);
 
+// for backwards-compat
 #define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1
 
+// work only with partial states, such as SWA KV cache or recurrent cache (e.g. Mamba)
+#define LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY 1
+
     typedef uint32_t llama_state_seq_flags;
 
     LLAMA_API size_t llama_state_seq_get_size_ext(