talk-llama : sync llama.cpp

examples/talk-llama/llama-arch.cpp

@@ -45,6 +45,9 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_GEMMA3N,          "gemma3n"          },
     { LLM_ARCH_STARCODER2,       "starcoder2"       },
     { LLM_ARCH_MAMBA,            "mamba"            },
+    { LLM_ARCH_MAMBA2,           "mamba2"           },
+    { LLM_ARCH_JAMBA,            "jamba"            },
+    { LLM_ARCH_FALCON_H1,        "falcon-h1"        },
     { LLM_ARCH_XVERSE,           "xverse"           },
     { LLM_ARCH_COMMAND_R,        "command-r"        },
     { LLM_ARCH_COHERE2,          "cohere2"          },
@@ -70,6 +73,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_ARWKV7,           "arwkv7"           },
     { LLM_ARCH_GRANITE,          "granite"          },
     { LLM_ARCH_GRANITE_MOE,      "granitemoe"       },
+    { LLM_ARCH_GRANITE_HYBRID,   "granitehybrid"    },
     { LLM_ARCH_CHAMELEON,        "chameleon"        },
     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
     { LLM_ARCH_PLM,              "plm"              },
@@ -77,6 +81,9 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DOTS1,            "dots1"            },
     { LLM_ARCH_ARCEE,            "arcee"            },
     { LLM_ARCH_ERNIE4_5,         "ernie4_5"         },
+    { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
+    { LLM_ARCH_SMOLLM3,          "smollm3"          },
+    { LLM_ARCH_LFM2,             "lfm2"             },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -149,7 +156,6 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
-    { LLM_KV_ATTENTION_LAYER_INDICES,                "%s.attention.layer_indices"                },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,      "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_DIMENSION_SECTIONS,   "%s.rope.dimension_sections"              },
@@ -170,6 +176,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_SSM_INNER_SIZE,     "%s.ssm.inner_size"     },
     { LLM_KV_SSM_STATE_SIZE,     "%s.ssm.state_size"     },
     { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
+    { LLM_KV_SSM_GROUP_COUNT,    "%s.ssm.group_count"    },
     { LLM_KV_SSM_DT_B_C_RMS,     "%s.ssm.dt_b_c_rms"     },
 
     { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
@@ -182,6 +189,8 @@ 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" },
+
     { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
     { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
     { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },
@@ -1004,6 +1013,77 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
         },
     },
+    {
+        LLM_ARCH_MAMBA2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_NORM,        "blk.%d.ssm_norm" },
+            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
+        },
+    },
+    {
+        LLM_ARCH_JAMBA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
+            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_DT_NORM,     "blk.%d.ssm_dt_norm" },
+            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_B_NORM,      "blk.%d.ssm_b_norm" },
+            { LLM_TENSOR_SSM_C_NORM,      "blk.%d.ssm_c_norm" },
+            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
+            { 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_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { 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_ARCH_FALCON_H1,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { 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_SSM_IN,          "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_NORM,        "blk.%d.ssm_norm" },
+            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_XVERSE,
         {
@@ -1564,6 +1644,43 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
         },
     },
+    {
+        LLM_ARCH_GRANITE_HYBRID,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
+            { LLM_TENSOR_OUTPUT,         "output" },
+            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
+            // mamba(2) ssm layers
+            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
+            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
+            // attention layers
+            { 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" },
+            // dense FFN
+            { LLM_TENSOR_FFN_NORM,       "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,       "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
+            // moe FFN
+            { LLM_TENSOR_FFN_NORM,       "blk.%d.ffn_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" },
+            // shared expert
+            { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,   "blk.%d.ffn_up_shexp" },
+        },
+    },
     {
         LLM_ARCH_CHAMELEON,
         {
@@ -1676,6 +1793,67 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_HUNYUAN_MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { 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" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
+            { 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_ARCH_SMOLLM3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
+            { LLM_TENSOR_OUTPUT,         "output" },
+            { 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_FFN_NORM,       "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,       "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
+        },
+    },
+    {
+        LLM_ARCH_LFM2,
+        {
+            { 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_ARCH_UNKNOWN,
         {
@@ -1760,7 +1938,11 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_FFN_ACT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_DIV}},
     {LLM_TENSOR_SSM_CONV1D,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
     {LLM_TENSOR_SSM_A,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}},
+    {LLM_TENSOR_SSM_DT_NORM,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SSM_B_NORM,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SSM_C_NORM,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_SSM_D,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SSM_NORM,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_TIME_MIX_LERP_X,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_TIME_MIX_LN,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_CHANNEL_MIX_LERP_K,         {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@@ -1839,6 +2021,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_CONVNEXT_PW1,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CONVNEXT_PW2,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CONVNEXT_GAMMA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_SHORTCONV_CONV,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
+    {LLM_TENSOR_SHORTCONV_INPROJ,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SHORTCONV_OUTPROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
 };
 
 LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
@@ -1894,6 +2079,7 @@ const llm_tensor_info & llm_tensor_info_for(llm_tensor tensor) {
 bool llm_arch_is_recurrent(const llm_arch & arch) {
     switch (arch) {
         case LLM_ARCH_MAMBA:
+        case LLM_ARCH_MAMBA2:
         case LLM_ARCH_RWKV6:
         case LLM_ARCH_RWKV6QWEN2:
         case LLM_ARCH_RWKV7:
@@ -1905,9 +2091,12 @@ bool llm_arch_is_recurrent(const llm_arch & arch) {
 }
 
 bool llm_arch_is_hybrid(const llm_arch & arch) {
-    // TODO: There are currently no hybrid models! Once there are, this will be
-    //  the place to identify them
     switch (arch) {
+        case LLM_ARCH_JAMBA:
+        case LLM_ARCH_FALCON_H1:
+        case LLM_ARCH_GRANITE_HYBRID:
+        case LLM_ARCH_LFM2:
+            return true;
         default:
             return false;
     }

examples/talk-llama/llama-arch.h

@@ -49,6 +49,9 @@ enum llm_arch {
     LLM_ARCH_GEMMA3N,
     LLM_ARCH_STARCODER2,
     LLM_ARCH_MAMBA,
+    LLM_ARCH_MAMBA2,
+    LLM_ARCH_JAMBA,
+    LLM_ARCH_FALCON_H1,
     LLM_ARCH_XVERSE,
     LLM_ARCH_COMMAND_R,
     LLM_ARCH_COHERE2,
@@ -74,6 +77,7 @@ enum llm_arch {
     LLM_ARCH_ARWKV7,
     LLM_ARCH_GRANITE,
     LLM_ARCH_GRANITE_MOE,
+    LLM_ARCH_GRANITE_HYBRID,
     LLM_ARCH_CHAMELEON,
     LLM_ARCH_WAVTOKENIZER_DEC,
     LLM_ARCH_PLM,
@@ -81,6 +85,9 @@ enum llm_arch {
     LLM_ARCH_DOTS1,
     LLM_ARCH_ARCEE,
     LLM_ARCH_ERNIE4_5,
+    LLM_ARCH_HUNYUAN_MOE,
+    LLM_ARCH_SMOLLM3,
+    LLM_ARCH_LFM2,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -153,7 +160,6 @@ enum llm_kv {
     LLM_KV_ATTENTION_SCALE,
     LLM_KV_ATTENTION_KEY_LENGTH_MLA,
     LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
-    LLM_KV_ATTENTION_LAYER_INDICES,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_DIMENSION_SECTIONS,
@@ -174,6 +180,7 @@ enum llm_kv {
     LLM_KV_SSM_CONV_KERNEL,
     LLM_KV_SSM_STATE_SIZE,
     LLM_KV_SSM_TIME_STEP_RANK,
+    LLM_KV_SSM_GROUP_COUNT,
     LLM_KV_SSM_DT_B_C_RMS,
 
     LLM_KV_WKV_HEAD_SIZE,
@@ -221,6 +228,8 @@ enum llm_kv {
 
     LLM_KV_CLASSIFIER_OUTPUT_LABELS,
 
+    LLM_KV_SHORTCONV_L_CACHE,
+
     // deprecated:
     LLM_KV_TOKENIZER_PREFIX_ID,
     LLM_KV_TOKENIZER_SUFFIX_ID,
@@ -291,8 +300,12 @@ enum llm_tensor {
     LLM_TENSOR_SSM_CONV1D,
     LLM_TENSOR_SSM_X,
     LLM_TENSOR_SSM_DT,
+    LLM_TENSOR_SSM_DT_NORM,
     LLM_TENSOR_SSM_A,
+    LLM_TENSOR_SSM_B_NORM,
+    LLM_TENSOR_SSM_C_NORM,
     LLM_TENSOR_SSM_D,
+    LLM_TENSOR_SSM_NORM,
     LLM_TENSOR_SSM_OUT,
     LLM_TENSOR_TIME_MIX_W0,
     LLM_TENSOR_TIME_MIX_W1,
@@ -386,6 +399,9 @@ enum llm_tensor {
     LLM_TENSOR_POS_NET_ATTN_K,
     LLM_TENSOR_POS_NET_ATTN_V,
     LLM_TENSOR_POS_NET_ATTN_OUT,
+    LLM_TENSOR_SHORTCONV_CONV,
+    LLM_TENSOR_SHORTCONV_INPROJ,
+    LLM_TENSOR_SHORTCONV_OUTPROJ,
 };
 
 enum llm_tensor_layer {

examples/talk-llama/llama-batch.cpp

@@ -166,6 +166,8 @@ bool llama_batch_allocr::init(
 
                 // note: tracking the other way around is not necessary for now
                 //seq_cpl[s0][s1] = true;
+
+                has_cpl = true;
             }
         }
     }
@@ -405,6 +407,10 @@ uint32_t llama_batch_allocr::get_n_outputs() const {
     return n_outputs;
 }
 
+uint32_t llama_batch_allocr::get_n_used() const {
+    return n_used;
+}
+
 std::vector<int32_t> & llama_batch_allocr::get_out_ids() {
     return out_ids;
 }
@@ -420,6 +426,8 @@ llama_pos llama_batch_allocr::seq_pos_max(llama_seq_id seq_id) const {
 void llama_batch_allocr::split_reset() {
     out_ids.clear();
 
+    n_used = 0;
+
     used.clear();
     used.resize(get_n_tokens(), false);
 
@@ -444,6 +452,7 @@ llama_ubatch llama_batch_allocr::split_simple(uint32_t n_ubatch) {
         idxs.push_back(cur_idx);
 
         used[cur_idx] = true;
+        ++n_used;
 
         ++cur_idx;
 
@@ -459,9 +468,17 @@ llama_ubatch llama_batch_allocr::split_simple(uint32_t n_ubatch) {
     return ubatch_add(idxs, idxs.size(), false);
 }
 
-llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch) {
+llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch, bool sequential) {
+    if (sequential && has_cpl) {
+        LLAMA_LOG_ERROR("%s: sequential split is not supported when there are coupled sequences in the input batch\n", __func__);
+
+        return {};
+    }
+
     std::vector<seq_set_t> cur_seq_set;
 
+    llama_seq_id last_seq_id = -1;
+
     // determine the non-overlapping sequence sets participating in this ubatch
     for (int32_t i = 0; i < batch.n_tokens; ++i) {
         if (used[i]) {
@@ -478,9 +495,16 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch) {
             }
         }
 
+        // accept only increasing sequence ids
+        if (sequential) {
+            add = add && (cur_seq_set.empty() || batch.seq_id[i][0] == last_seq_id + 1);
+        }
+
         if (add) {
             cur_seq_set.push_back(seq_set[i]);
 
+            last_seq_id = batch.seq_id[i][0];
+
             if (cur_seq_set.size() > n_ubatch) {
                 break;
             }
@@ -529,6 +553,7 @@ llama_ubatch llama_batch_allocr::split_equal(uint32_t n_ubatch) {
             idxs_per_seq[s].push_back(idx);
 
             used[idx] = true;
+            ++n_used;
 
             ++cur_idx[s];
         }
@@ -570,6 +595,7 @@ llama_ubatch llama_batch_allocr::split_seq(uint32_t n_ubatch) {
         idxs.push_back(cur_idx);
 
         used[cur_idx] = true;
+        ++n_used;
 
         if (idxs.size() >= n_ubatch) {
             break;

examples/talk-llama/llama-batch.h

@@ -54,6 +54,7 @@ public:
 
     uint32_t get_n_tokens()  const;
     uint32_t get_n_outputs() const;
+    uint32_t get_n_used()    const;
 
     // the array of output indices in the order they were encountered during the ubatch splitting
     std::vector<int32_t> & get_out_ids();
@@ -69,7 +70,8 @@ public:
     llama_ubatch split_simple(uint32_t n_ubatch);
 
     // make ubatches of equal-length sequences sets
-    llama_ubatch split_equal(uint32_t n_ubatch);
+    // if sequential == true, the tokens in the ubatch will have increasing sequential sequence ids
+    llama_ubatch split_equal(uint32_t n_ubatch, bool sequential);
 
     // sequence-set-wise split - each ubatch contains a single sequence-set
     llama_ubatch split_seq(uint32_t n_ubatch);
@@ -112,6 +114,9 @@ private:
     using pos_set_t = std::set<llama_pos>;
     using seq_cpl_t = std::vector<bool>;
 
+    // helper flag to quickly determine if there are any coupled sequences in the batch
+    bool has_cpl;
+
     std::vector<pos_set_t> seq_pos; // seq_pos[s]: the set of positions in sequence s
     std::vector<seq_cpl_t> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1
 
@@ -125,6 +130,8 @@ private:
     // batch indices of the output
     std::vector<int32_t> out_ids;
 
+    uint32_t n_used;
+
     // used[i] indicates if token i has already been used in a previous ubatch
     std::vector<bool> used;
 

examples/talk-llama/llama-chat.cpp

@@ -64,6 +64,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "bailing",           LLM_CHAT_TEMPLATE_BAILING           },
     { "llama4",            LLM_CHAT_TEMPLATE_LLAMA4            },
     { "smolvlm",           LLM_CHAT_TEMPLATE_SMOLVLM           },
+    { "hunyuan-moe",       LLM_CHAT_TEMPLATE_HUNYUAN_MOE       },
 };
 
 llm_chat_template llm_chat_template_from_str(const std::string & name) {
@@ -185,6 +186,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_LLAMA4;
     } else if (tmpl_contains("<|endofuserprompt|>")) {
         return LLM_CHAT_TEMPLATE_DOTS1;
+    } else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
+        return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -665,6 +668,18 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|response|>";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_MOE) {
+        // tencent/Hunyuan-A13B-Instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|startoftext|>" << message->content << "<|extra_4|>";
+            } else if (role == "assistant") {
+                ss << "<|startoftext|>" << message->content << "<|eos|>";
+            } else {
+                ss << "<|startoftext|>" << message->content << "<|extra_0|>";
+            }
+        }
     } else {
         // template not supported
         return -1;

examples/talk-llama/llama-chat.h

@@ -44,6 +44,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_LLAMA4,
     LLM_CHAT_TEMPLATE_SMOLVLM,
     LLM_CHAT_TEMPLATE_DOTS1,
+    LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 

examples/talk-llama/llama-graph.cpp

@@ -281,19 +281,22 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
-    if (self_kq_mask) {
-        mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
-    }
+    mctx->set_input_k_idxs(self_k_idxs, ubatch);
+    mctx->set_input_v_idxs(self_v_idxs, ubatch);
+
+    mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
 }
 
 void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
-    if (self_kq_mask) {
-        mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
-    }
+    mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
+    mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
 
-    if (self_kq_mask_swa) {
-        mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
-    }
+    mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+
+    mctx->get_swa()->set_input_k_idxs(self_k_idxs_swa, ubatch);
+    mctx->get_swa()->set_input_v_idxs(self_v_idxs_swa, ubatch);
+
+    mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
 }
 
 void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
@@ -333,27 +336,8 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
-    if (self_kq_mask) {
-        mctx->get_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
-    }
-
-    const int64_t n_rs = mctx->get_recr()->get_n_rs();
-
-    if (s_copy) {
-        GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
-        int32_t * data = (int32_t *) s_copy->data;
-
-        // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
-        for (uint32_t i = 0; i < n_rs; ++i) {
-            data[i] = mctx->get_recr()->s_copy(i);
-        }
-    }
-}
-
-void llm_graph_input_one::set_input(const llama_ubatch *) {
-    GGML_ASSERT(one && ggml_nelements(one) == 1);
-    float f_one = 1.0f;
-    ggml_backend_tensor_set(one, &f_one, 0, sizeof(float));
+    inp_attn->set_input(ubatch);
+    inp_rs->set_input(ubatch);
 }
 
 //
@@ -987,33 +971,6 @@ ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_t
     return pos_bias;
 }
 
-llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
-    const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
-
-    auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mctx_cur);
-
-    {
-        GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Hybrid recurrent is not supported with SWA attention layers");
-
-        const auto n_kv = inp->mctx->get_attn()->get_n_kv();
-
-        inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask, "KQ_mask", -1);
-        ggml_set_input(inp->self_kq_mask);
-
-        inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
-    }
-
-    {
-        const auto n_rs = mctx_cur->get_recr()->get_n_rs();
-
-        inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
-        ggml_set_input(inp->s_copy);
-    }
-
-    return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
-}
-
 ggml_tensor * llm_graph_context::build_attn_mha(
          ggml_cgraph * gf,
          ggml_tensor * q,
@@ -1135,8 +1092,7 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
     auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams);
 
     // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch
-    inp->kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-    //cb(inp_kq_mask, "KQ_mask", -1);
+    inp->kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
     ggml_set_input(inp->kq_mask);
 
     inp->kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->kq_mask, GGML_TYPE_F16) : inp->kq_mask;
@@ -1188,8 +1144,12 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
-llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
-    const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
+static std::unique_ptr<llm_graph_input_attn_kv_unified> build_attn_inp_kv_unified_impl(
+           ggml_context * ctx0,
+     const llama_ubatch & ubatch,
+    const llama_hparams & hparams,
+    const llama_cparams & cparams,
+    const llama_kv_cache_unified_context * mctx_cur) {
 
     auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, mctx_cur);
 
@@ -1197,14 +1157,25 @@ llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified()
         GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
 
         const auto n_kv = mctx_cur->get_n_kv();
+        const auto n_tokens = ubatch.n_tokens;
+
+        inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs = mctx_cur->build_input_v_idxs(ctx0, ubatch);
 
-        inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask, "KQ_mask", -1);
+        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
         ggml_set_input(inp->self_kq_mask);
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
     }
 
+    return inp;
+}
+
+llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
+
+    auto inp = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
+
     return (llm_graph_input_attn_kv_unified *) res->add_input(std::move(inp));
 }
 
@@ -1226,12 +1197,15 @@ ggml_tensor * llm_graph_context::build_attn(
     ggml_build_forward_expand(gf, k_cur);
     ggml_build_forward_expand(gf, v_cur);
 
-    const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
+    const auto * mctx_cur = inp->mctx;
 
     // store to KV cache
     {
-        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
-        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
+        const auto & k_idxs = inp->get_k_idxs();
+        const auto & v_idxs = inp->get_v_idxs();
+
+        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
+        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
     }
 
     const auto & kq_mask = inp->get_kq_mask();
@@ -1282,7 +1256,7 @@ ggml_tensor * llm_graph_context::build_attn(
         ggml_build_forward_expand(gf, v_cur);
     }
 
-    const auto * mctx_iswa = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
+    const auto * mctx_iswa = inp->mctx;
 
     const bool is_swa = hparams.is_swa(il);
 
@@ -1290,11 +1264,15 @@ ggml_tensor * llm_graph_context::build_attn(
 
     // optionally store to KV cache
     if (k_cur) {
-        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
+        const auto & k_idxs = is_swa ? inp->get_k_idxs_swa() : inp->get_k_idxs();
+
+        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
     }
 
     if (v_cur) {
-        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
+        const auto & v_idxs = is_swa ? inp->get_v_idxs_swa() : inp->get_v_idxs();
+
+        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il));
     }
 
     const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
@@ -1326,7 +1304,7 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
 
     const int32_t n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
 
-    inp->cross_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+    inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
     ggml_set_input(inp->cross_kq_mask);
 
     inp->cross_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->cross_kq_mask, GGML_TYPE_F16) : inp->cross_kq_mask;
@@ -1376,56 +1354,9 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
-ggml_tensor * llm_graph_context::build_attn(
-        llm_graph_input_mem_hybrid * inp,
-        ggml_cgraph * gf,
-        ggml_tensor * wo,
-        ggml_tensor * wo_b,
-        ggml_tensor * q_cur,
-        ggml_tensor * k_cur,
-        ggml_tensor * v_cur,
-        ggml_tensor * kq_b,
-        ggml_tensor * v_mla,
-            float     kq_scale,
-            int       il) const {
-    // these nodes are added to the graph together so that they are not reordered
-    // by doing so, the number of splits in the graph is reduced
-    ggml_build_forward_expand(gf, q_cur);
-    ggml_build_forward_expand(gf, k_cur);
-    ggml_build_forward_expand(gf, v_cur);
-
-    const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_attn();
-
-    // store to KV cache
-    {
-        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
-        ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
-    }
-
-    const auto & kq_mask = inp->get_kq_mask();
-
-    ggml_tensor * q = q_cur;
-    ggml_tensor * k = mctx_cur->get_k(ctx0, il);
-    ggml_tensor * v = mctx_cur->get_v(ctx0, il);
-
-    ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
-    cb(cur, "kqv_out", il);
-
-    if (wo) {
-        cur = build_lora_mm(wo, cur);
-        if (arch == LLM_ARCH_GLM4) {
-            // GLM4 seems to have numerical issues with half-precision accumulators
-            ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
-        }
-    }
-
-    if (wo_b) {
-        cur = ggml_add(ctx0, cur, wo_b);
-    }
-
-    return cur;
-}
-
+// TODO: maybe separate the inner implementation into a separate function
+//       like with the non-sliding window equivalent
+//       once sliding-window hybrid caches are a thing.
 llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
     const auto * mctx_cur = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
 
@@ -1434,8 +1365,10 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
     {
         const auto n_kv = mctx_cur->get_base()->get_n_kv();
 
-        inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask, "KQ_mask", -1);
+        inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs = mctx_cur->get_base()->build_input_v_idxs(ctx0, ubatch);
+
+        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
         ggml_set_input(inp->self_kq_mask);
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
@@ -1446,8 +1379,10 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
 
         const auto n_kv = mctx_cur->get_swa()->get_n_kv();
 
-        inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        //cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
+        inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
+        inp->self_v_idxs_swa = mctx_cur->get_swa()->build_input_v_idxs(ctx0, ubatch);
+
+        inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
         ggml_set_input(inp->self_kq_mask_swa);
 
         inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
@@ -1466,7 +1401,7 @@ ggml_tensor * llm_graph_context::build_rs(
            uint32_t   kv_head,
            uint32_t   kv_size,
             int32_t   rs_zero,
-               bool   avoid_copies) const {
+        const llm_graph_get_rows_fn & get_state_rows) const {
 
     ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_size);
 
@@ -1475,19 +1410,11 @@ ggml_tensor * llm_graph_context::build_rs(
     ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
     ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
 
-    ggml_tensor * output_states;
-
-    if (!avoid_copies) {
-        // copy states
-        // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
-        // {state_size, kv_size} -> {state_size, n_seqs}
-        output_states = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
-        ggml_build_forward_expand(gf, output_states);
-    } else {
-        // FIXME: make the gathering operation happen before the copy below
-        //        (maybe with an optional lambda function passed as a parameter instead of `avoid_copies`?)
-        output_states = states;
-    }
+    // copy states
+    // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
+    // {state_size, kv_size} -> {state_size, n_seqs}
+    ggml_tensor * output_states = get_state_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
+    ggml_build_forward_expand(gf, output_states);
 
     // copy extra states which won't be changed further (between n_seqs and n_kv)
     ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_kv - n_seqs, n_seqs*state_copy->nb[0]));
@@ -1499,8 +1426,9 @@ ggml_tensor * llm_graph_context::build_rs(
     return output_states;
 }
 
-llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
-    const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
+static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
+           ggml_context * ctx0,
+    const llama_memory_recurrent_context * mctx_cur) {
 
     auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);
 
@@ -1509,31 +1437,27 @@ llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
     inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
     ggml_set_input(inp->s_copy);
 
-    return (llm_graph_input_rs *) res->add_input(std::move(inp));
+    return inp;
 }
 
-ggml_tensor * llm_graph_context::build_rs(
-        llm_graph_input_rs * inp,
-        ggml_cgraph * gf,
-        ggml_tensor * s,
-            int32_t   state_size,
-            int32_t   n_seqs,
-               bool   avoid_copies) const {
+llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
     const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
 
-    return build_rs(gf, s, inp->s_copy, state_size, n_seqs, mctx_cur->get_n_rs(), mctx_cur->get_head(), mctx_cur->get_size(), mctx_cur->get_rs_z(), avoid_copies);
+    auto inp = build_rs_inp_impl(ctx0, mctx_cur);
+
+    return (llm_graph_input_rs *) res->add_input(std::move(inp));
 }
 
 ggml_tensor * llm_graph_context::build_rs(
-        llm_graph_input_mem_hybrid * inp,
+        llm_graph_input_rs * inp,
         ggml_cgraph * gf,
         ggml_tensor * s,
             int32_t   state_size,
             int32_t   n_seqs,
-               bool   avoid_copies) const {
-    const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
+        const llm_graph_get_rows_fn & get_state_rows) const {
+    const auto * kv_state = inp->mctx;
 
-    return build_rs(gf, s, inp->s_copy, state_size, n_seqs, mctx_cur->get_n_rs(), mctx_cur->get_head(), mctx_cur->get_size(), mctx_cur->get_rs_z(), avoid_copies);
+    return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), get_state_rows);
 }
 
 ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
@@ -1578,6 +1502,17 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
     );
 }
 
+llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
+    const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
+
+    auto inp_rs   = build_rs_inp_impl(ctx0, mctx_cur->get_recr());
+    auto inp_attn = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
+
+    auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
+
+    return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
+}
+
 void llm_graph_context::build_pooling(
         ggml_cgraph * gf,
         ggml_tensor * cls,

examples/talk-llama/llama-graph.h

@@ -228,8 +228,8 @@ public:
 
     ggml_tensor * get_kq_mask() const { return kq_mask_cnv; }
 
-    ggml_tensor * kq_mask     = nullptr; // F32 [n_tokens, n_batch]
-    ggml_tensor * kq_mask_cnv = nullptr; //     [n_tokens, n_batch]
+    ggml_tensor * kq_mask     = nullptr; // F32 [n_tokens, n_batch, 1, 1]
+    ggml_tensor * kq_mask_cnv = nullptr; //     [n_tokens, n_batch, 1, 1]
 
     const llama_hparams & hparams;
     const llama_cparams & cparams;
@@ -249,10 +249,16 @@ public:
 
     void set_input(const llama_ubatch * ubatch) override;
 
+    ggml_tensor * get_k_idxs() const { return self_k_idxs; }
+    ggml_tensor * get_v_idxs() const { return self_v_idxs; }
+
     ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
 
-    ggml_tensor * self_kq_mask     = nullptr; // F32 [n_kv, n_batch]
-    ggml_tensor * self_kq_mask_cnv = nullptr; //     [n_kv, n_batch]
+    ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs = nullptr; // I64 [n_batch]
+
+    ggml_tensor * self_kq_mask     = nullptr; // F32 [n_kv, n_batch, 1, 1]
+    ggml_tensor * self_kq_mask_cnv = nullptr; //     [n_kv, n_batch, 1, 1]
 
     const llama_hparams & hparams;
     const llama_cparams & cparams;
@@ -274,13 +280,23 @@ public:
 
     void set_input(const llama_ubatch * ubatch) override;
 
+    ggml_tensor * get_k_idxs()     const { return self_k_idxs; }
+    ggml_tensor * get_v_idxs()     const { return self_v_idxs; }
+    ggml_tensor * get_k_idxs_swa() const { return self_k_idxs_swa; }
+    ggml_tensor * get_v_idxs_swa() const { return self_v_idxs_swa; }
+
     ggml_tensor * get_kq_mask()     const { return self_kq_mask_cnv; }
     ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; }
 
-    ggml_tensor * self_kq_mask         = nullptr; // F32 [n_kv, n_batch]
-    ggml_tensor * self_kq_mask_cnv     = nullptr; //     [n_kv, n_batch]
-    ggml_tensor * self_kq_mask_swa     = nullptr; // F32 [n_kv, n_batch]
-    ggml_tensor * self_kq_mask_swa_cnv = nullptr; //     [n_kv, n_batch]
+    ggml_tensor * self_k_idxs     = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs     = nullptr; // I64 [n_batch]
+    ggml_tensor * self_k_idxs_swa = nullptr; // I64 [n_batch]
+    ggml_tensor * self_v_idxs_swa = nullptr; // I64 [n_batch]
+
+    ggml_tensor * self_kq_mask         = nullptr; // F32 [n_kv, n_batch, 1, 1]
+    ggml_tensor * self_kq_mask_cnv     = nullptr; //     [n_kv, n_batch, 1, 1]
+    ggml_tensor * self_kq_mask_swa     = nullptr; // F32 [n_kv, n_batch, 1, 1]
+    ggml_tensor * self_kq_mask_swa_cnv = nullptr; //     [n_kv, n_batch, 1, 1]
 
     const llama_hparams & hparams;
     const llama_cparams & cparams;
@@ -297,8 +313,8 @@ public:
 
     ggml_tensor * get_kq_mask_cross() const { return cross_kq_mask_cnv; }
 
-    ggml_tensor * cross_kq_mask     = nullptr; // F32 [n_outputs_enc, n_batch]
-    ggml_tensor * cross_kq_mask_cnv = nullptr; // F32 [n_outputs_enc, n_batch]
+    ggml_tensor * cross_kq_mask     = nullptr; // F32 [n_outputs_enc, n_batch, 1, 1]
+    ggml_tensor * cross_kq_mask_cnv = nullptr; // F32 [n_outputs_enc, n_batch, 1, 1]
 
     const llama_cross * cross = nullptr;
 };
@@ -306,41 +322,25 @@ public:
 class llm_graph_input_mem_hybrid : public llm_graph_input_i {
 public:
     llm_graph_input_mem_hybrid(
-            const llama_hparams & hparams,
-            const llama_cparams & cparams,
-            const llama_memory_hybrid_context * mctx) :
-        hparams(hparams),
-        cparams(cparams),
-        mctx(mctx) {
-    }
+            std::unique_ptr<llm_graph_input_attn_kv_unified> inp_attn,
+            std::unique_ptr<llm_graph_input_rs>              inp_rs,
+            const llama_memory_hybrid_context *              mctx) :
+        inp_attn(std::move(inp_attn)),
+        inp_rs(std::move(inp_rs)),
+        mctx(mctx) { }
     virtual ~llm_graph_input_mem_hybrid() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
-    ggml_tensor * s_copy; // I32 [kv_size]
+    std::unique_ptr<llm_graph_input_attn_kv_unified> inp_attn;
+    std::unique_ptr<llm_graph_input_rs>              inp_rs;
 
-    ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
-
-    ggml_tensor * self_kq_mask     = nullptr; // F32 [n_kv, n_batch]
-    ggml_tensor * self_kq_mask_cnv = nullptr; //     [n_kv, n_batch]
-
-    const llama_hparams & hparams;
-    const llama_cparams & cparams;
+    llm_graph_input_attn_kv_unified * get_attn() const { return inp_attn.get(); }
+    llm_graph_input_rs              * get_recr() const { return inp_rs.get(); }
 
     const llama_memory_hybrid_context * mctx;
 };
 
-// TODO: remove this when ggml_scale_add is implemented
-class llm_graph_input_one : public llm_graph_input_i {
-public:
-    llm_graph_input_one() {}
-    virtual ~llm_graph_input_one() = default;
-
-    void set_input(const llama_ubatch *) override;
-
-    ggml_tensor * one = nullptr; // F32
-};
-
 //
 // llm_graph_result
 //
@@ -424,6 +424,9 @@ struct llm_graph_params {
     const llm_graph_cb & cb;
 };
 
+// used in build_rs to properly order writes and avoid unnecessary copies
+using llm_graph_get_rows_fn = std::function<ggml_tensor * (ggml_context *, ggml_tensor * states, ggml_tensor * ids)>;
+
 struct llm_graph_context {
     const llm_arch arch;
 
@@ -554,8 +557,6 @@ struct llm_graph_context {
     ggml_tensor * build_inp_pos_bucket_dec() const;
     ggml_tensor * build_pos_bias(ggml_tensor * pos_bucket, ggml_tensor * attn_rel_b) const;
 
-    llm_graph_input_mem_hybrid * build_inp_mem_hybrid() const;
-
     //
     // attention
     //
@@ -631,18 +632,6 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
-    ggml_tensor * build_attn(
-            llm_graph_input_mem_hybrid * inp,
-            ggml_cgraph * gf,
-            ggml_tensor * wo,
-            ggml_tensor * wo_b,
-            ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
-            ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
-            ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
-            ggml_tensor * kq_b,
-            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
-                  float   kq_scale,
-                    int   il) const;
     //
     // recurrent
     //
@@ -663,7 +652,7 @@ struct llm_graph_context {
                uint32_t   kv_head,
                uint32_t   kv_size,
                 int32_t   rs_zero,
-                   bool   avoid_copies = false) const;
+            const llm_graph_get_rows_fn & get_state_rows = ggml_get_rows) const;
 
     llm_graph_input_rs * build_rs_inp() const;
 
@@ -673,15 +662,7 @@ struct llm_graph_context {
             ggml_tensor * s,
                 int32_t   state_size,
                 int32_t   n_seqs,
-                   bool   avoid_copies = false) const;
-
-    ggml_tensor * build_rs(
-            llm_graph_input_mem_hybrid * inp,
-            ggml_cgraph * gf,
-            ggml_tensor * s,
-                int32_t   state_size,
-                int32_t   n_seqs,
-                   bool   avoid_copies = false) const;
+            const llm_graph_get_rows_fn & get_state_rows = ggml_get_rows) const;
 
     ggml_tensor * build_rwkv_token_shift_load(
         llm_graph_input_rs * inp,
@@ -693,6 +674,11 @@ struct llm_graph_context {
              ggml_tensor * token_shift,
       const llama_ubatch & ubatch,
                      int   il) const;
+    //
+    // hybrid
+    //
+
+    llm_graph_input_mem_hybrid * build_inp_mem_hybrid() const;
 
     //
     // pooling

examples/talk-llama/llama-hparams.cpp

@@ -71,9 +71,15 @@ uint32_t llama_hparams::n_embd_r() const {
         return token_shift_count * n_embd;
     }
 
+    if (n_shortconv_l_cache != 0) {
+        // for LFM2 models
+        return n_embd * (n_shortconv_l_cache - 1);
+    }
+
     // TODO: maybe support other convolution strides than 1
     // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
-    return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
+    // Corresponds to Mamba's conv_states size
+    return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * (ssm_d_inner + 2*ssm_n_group*ssm_d_state);
 }
 
 uint32_t llama_hparams::n_embd_s() const {

examples/talk-llama/llama-hparams.h

@@ -55,6 +55,8 @@ struct llama_hparams {
     struct llama_hparams_posnet   posnet;
     struct llama_hparams_convnext convnext;
 
+    uint32_t n_shortconv_l_cache  = 0;
+
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
@@ -114,6 +116,7 @@ struct llama_hparams {
     uint32_t ssm_d_inner = 0;
     uint32_t ssm_d_state = 0;
     uint32_t ssm_dt_rank = 0;
+    uint32_t ssm_n_group = 0;
 
     // for hybrid state space models
     std::array<bool, LLAMA_MAX_LAYERS> recurrent_layer_arr;

examples/talk-llama/llama-kv-cache-unified-iswa.cpp

@@ -113,20 +113,25 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
             ubatches.push_back(std::move(ubatch)); // NOLINT
         }
 
-        auto heads_base = kv_base->prepare(ubatches);
-        if (heads_base.empty()) {
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
             break;
         }
 
-        auto heads_swa = kv_swa->prepare(ubatches);
-        if (heads_swa.empty()) {
+        auto sinfos_base = kv_base->prepare(ubatches);
+        if (sinfos_base.empty()) {
             break;
         }
 
-        assert(heads_base.size() == heads_swa.size());
+        auto sinfos_swa = kv_swa->prepare(ubatches);
+        if (sinfos_swa.empty()) {
+            break;
+        }
+
+        assert(sinfos_base.size() == sinfos_swa.size());
 
         return std::make_unique<llama_kv_cache_unified_iswa_context>(
-                this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
+                this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
     } while (false);
 
     // if it fails, try equal split
@@ -135,7 +140,7 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
 
         std::vector<llama_ubatch> ubatches;
         while (true) {
-            auto ubatch = balloc.split_equal(n_ubatch);
+            auto ubatch = balloc.split_equal(n_ubatch, false);
 
             if (ubatch.n_tokens == 0) {
                 break;
@@ -144,20 +149,25 @@ llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_all
             ubatches.push_back(std::move(ubatch)); // NOLINT
         }
 
-        auto heads_base = kv_base->prepare(ubatches);
-        if (heads_base.empty()) {
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
+        auto sinfos_base = kv_base->prepare(ubatches);
+        if (sinfos_base.empty()) {
             break;
         }
 
-        auto heads_swa = kv_swa->prepare(ubatches);
-        if (heads_swa.empty()) {
+        auto sinfos_swa = kv_swa->prepare(ubatches);
+        if (sinfos_swa.empty()) {
             break;
         }
 
-        assert(heads_base.size() == heads_swa.size());
+        assert(sinfos_base.size() == sinfos_swa.size());
 
         return std::make_unique<llama_kv_cache_unified_iswa_context>(
-                this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
+                this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
     } while (false);
 
     // TODO: if we fail again, we should attempt different splitting strategies
@@ -220,13 +230,13 @@ llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
 
 llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
         llama_kv_cache_unified_iswa * kv,
-        std::vector<uint32_t> heads_base,
-        std::vector<uint32_t> heads_swa,
+        slot_info_vec_t sinfos_base,
+        slot_info_vec_t sinfos_swa,
         std::vector<llama_ubatch> ubatches) :
     ubatches(std::move(ubatches)),
     // note: here we copy the ubatches. not sure if this is ideal
-    ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(heads_base), this->ubatches)),
-    ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(heads_swa),  this->ubatches)),
+    ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(sinfos_base), this->ubatches)),
+    ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(sinfos_swa),  this->ubatches)),
     status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
 }
 

examples/talk-llama/llama-kv-cache-unified-iswa.h

@@ -74,6 +74,8 @@ private:
 
 class llama_kv_cache_unified_iswa_context : public llama_memory_context_i {
 public:
+    using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
+
     // used for errors
     llama_kv_cache_unified_iswa_context(llama_memory_status status);
 
@@ -90,8 +92,8 @@ public:
     // used to create a batch processing context from a batch
     llama_kv_cache_unified_iswa_context(
             llama_kv_cache_unified_iswa * kv,
-            std::vector<uint32_t> heads_base,
-            std::vector<uint32_t> heads_swa,
+            slot_info_vec_t sinfos_base,
+            slot_info_vec_t sinfos_swa,
             std::vector<llama_ubatch> ubatches);
 
     virtual ~llama_kv_cache_unified_iswa_context();

examples/talk-llama/llama-kv-cache-unified.cpp

@@ -156,6 +156,13 @@ llama_kv_cache_unified::llama_kv_cache_unified(
 
     const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
     debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;
+
+    const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
+    supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) : 0;
+
+    if (!supports_set_rows) {
+        LLAMA_LOG_WARN("%s: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility\n", __func__);
+    }
 }
 
 void llama_kv_cache_unified::clear(bool data) {
@@ -353,13 +360,18 @@ llama_memory_context_ptr llama_kv_cache_unified::init_batch(
             ubatches.push_back(std::move(ubatch)); // NOLINT
         }
 
-        auto heads = prepare(ubatches);
-        if (heads.empty()) {
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
+        auto sinfos = prepare(ubatches);
+        if (sinfos.empty()) {
             break;
         }
 
         return std::make_unique<llama_kv_cache_unified_context>(
-                this, std::move(heads), std::move(ubatches));
+                this, std::move(sinfos), std::move(ubatches));
     } while (false);
 
     return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
@@ -402,12 +414,13 @@ llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lct
     return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo));
 }
 
-llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
-    llama_kv_cache_unified::ubatch_heads res;
+llama_kv_cache_unified::slot_info_vec_t llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
+    llama_kv_cache_unified::slot_info_vec_t res;
 
     struct state {
         uint32_t head_old; // old position of the head, before placing the ubatch
-        uint32_t head_new; // new position of the head, after placing the ubatch
+
+        slot_info sinfo; // slot info for the ubatch
 
         llama_kv_cells_unified cells; // copy of the old cells, before placing the ubatch
     };
@@ -418,26 +431,29 @@ llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::
     bool success = true;
 
     for (const auto & ubatch : ubatches) {
+        // non-continuous slots require support for ggml_set_rows()
+        const bool cont = supports_set_rows ? false : true;
+
         // only find a suitable slot for the ubatch. don't modify the cells yet
-        const int32_t head_new = find_slot(ubatch);
-        if (head_new < 0) {
+        const auto sinfo_new = find_slot(ubatch, cont);
+        if (sinfo_new.empty()) {
             success = false;
             break;
         }
 
         // remeber the position that we found
-        res.push_back(head_new);
+        res.push_back(sinfo_new);
 
         // store the old state of the cells in the recovery stack
-        states.push_back({head, (uint32_t) head_new, cells.cp(head_new, ubatch.n_tokens)});
+        states.push_back({head, sinfo_new, cells.cp(sinfo_new.idxs)});
 
         // now emplace the ubatch
-        apply_ubatch(head_new, ubatch);
+        apply_ubatch(sinfo_new, ubatch);
     }
 
     // iterate backwards and restore the cells to their original state
     for (auto it = states.rbegin(); it != states.rend(); ++it) {
-        cells.set(it->head_new, it->cells);
+        cells.set(it->sinfo.idxs, it->cells);
         head = it->head_old;
     }
 
@@ -539,7 +555,7 @@ bool llama_kv_cache_unified::update(llama_context * lctx, bool do_shift, const d
     return updated;
 }
 
-int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
+llama_kv_cache_unified::slot_info llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch, bool cont) const {
     const uint32_t n_tokens = ubatch.n_tokens;
 
     uint32_t head_cur = this->head;
@@ -552,7 +568,7 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
 
     if (n_tokens > cells.size()) {
         LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
-        return -1;
+        return { };
     }
 
     if (debug > 0) {
@@ -615,15 +631,26 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
 
     uint32_t n_tested = 0;
 
+    // for continuous slots, we test that all tokens in the ubatch fit, starting from the current head
+    // for non-continuous slots, we test the tokens one by one
+    const uint32_t n_test = cont ? n_tokens : 1;
+
+    slot_info res;
+
+    auto & idxs = res.idxs;
+
+    idxs.reserve(n_tokens);
+
     while (true) {
-        if (head_cur + n_tokens > cells.size()) {
+        if (head_cur + n_test > cells.size()) {
             n_tested += cells.size() - head_cur;
             head_cur = 0;
             continue;
         }
 
-        bool found = true;
-        for (uint32_t i = 0; i < n_tokens; i++) {
+        for (uint32_t i = 0; i < n_test; i++) {
+            const auto idx = head_cur;
+
             //const llama_pos    pos    = ubatch.pos[i];
             //const llama_seq_id seq_id = ubatch.seq_id[i][0];
 
@@ -633,19 +660,19 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
             //    - (disabled) mask causally, if the sequence is the same as the one we are inserting
             //    - mask SWA, using current max pos for that sequence in the cache
             //                always insert in the cell with minimum pos
-            bool can_use = cells.is_empty(head_cur + i);
+            bool can_use = cells.is_empty(idx);
 
-            if (!can_use && cells.seq_count(head_cur + i) == 1) {
-                const llama_pos pos_cell = cells.pos_get(head_cur + i);
+            if (!can_use && cells.seq_count(idx) == 1) {
+                const llama_pos pos_cell = cells.pos_get(idx);
 
                 // (disabled) causal mask
                 // note: it's better to purge any "future" tokens beforehand
-                //if (cells.seq_has(head_cur + i, seq_id)) {
+                //if (cells.seq_has(idx, seq_id)) {
                 //    can_use = pos_cell >= pos;
                 //}
 
                 if (!can_use) {
-                    const llama_seq_id seq_id_cell = cells.seq_get(head_cur + i);
+                    const llama_seq_id seq_id_cell = cells.seq_get(idx);
 
                     // SWA mask
                     if (is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
@@ -654,28 +681,39 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const {
                 }
             }
 
-            if (!can_use) {
-                found = false;
-                head_cur += i + 1;
-                n_tested += i + 1;
+            head_cur++;
+            n_tested++;
+
+            if (can_use) {
+                idxs.push_back(idx);
+            } else {
                 break;
             }
         }
 
-        if (found) {
+        if (idxs.size() == n_tokens) {
             break;
         }
 
+        if (cont) {
+            idxs.clear();
+        }
+
         if (n_tested >= cells.size()) {
             //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
-            return -1;
+            return { };
         }
     }
 
-    return head_cur;
+    // we didn't find a suitable slot - return empty result
+    if (idxs.size() < n_tokens) {
+        res.clear();
+    }
+
+    return res;
 }
 
-void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch) {
+void llama_kv_cache_unified::apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch) {
     // keep track of the max sequence position that we would overwrite with this ubatch
     // for non-SWA cache, this would be always empty
     llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ];
@@ -683,22 +721,26 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
         seq_pos_max_rm[s] = -1;
     }
 
+    assert(ubatch.n_tokens == sinfo.idxs.size());
+
     for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
-        if (!cells.is_empty(head_cur + i)) {
-            assert(cells.seq_count(head_cur + i) == 1);
+        const auto idx = sinfo.idxs.at(i);
 
-            const llama_seq_id seq_id = cells.seq_get(head_cur + i);
-            const llama_pos    pos    = cells.pos_get(head_cur + i);
+        if (!cells.is_empty(idx)) {
+            assert(cells.seq_count(idx) == 1);
+
+            const llama_seq_id seq_id = cells.seq_get(idx);
+            const llama_pos    pos    = cells.pos_get(idx);
 
             seq_pos_max_rm[seq_id] = std::max(seq_pos_max_rm[seq_id], pos);
 
-            cells.rm(head_cur + i);
+            cells.rm(idx);
         }
 
-        cells.pos_set(head_cur + i, ubatch.pos[i]);
+        cells.pos_set(idx, ubatch.pos[i]);
 
         for (int32_t s = 0; s < ubatch.n_seq_id[i]; s++) {
-            cells.seq_add(head_cur + i, ubatch.seq_id[i][s]);
+            cells.seq_add(idx, ubatch.seq_id[i][s]);
         }
     }
 
@@ -719,7 +761,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch
     }
 
     // move the head at the end of the slot
-    head = head_cur + ubatch.n_tokens;
+    head = sinfo.idxs.back() + 1;
 }
 
 bool llama_kv_cache_unified::get_can_shift() const {
@@ -772,47 +814,133 @@ ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint
             0);
 }
 
-ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const {
+ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * k = layers[ikv].k;
 
+    const int64_t n_embd_k_gqa = k->ne[0];
     const int64_t n_tokens = k_cur->ne[2];
 
+    k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
+
+    if (k_idxs && supports_set_rows) {
+        return ggml_set_rows(ctx, k, k_cur, k_idxs);
+    }
+
+    // TODO: fallback to old ggml_cpy() method for backwards compatibility
+    //       will be removed when ggml_set_rows() is adopted by all backends
+
     ggml_tensor * k_view = ggml_view_1d(ctx, k,
-            n_tokens*hparams.n_embd_k_gqa(il),
-            ggml_row_size(k->type, hparams.n_embd_k_gqa(il))*head_cur);
+            n_tokens*n_embd_k_gqa,
+            ggml_row_size(k->type, n_embd_k_gqa)*sinfo.head());
 
     return ggml_cpy(ctx, k_cur, k_view);
 }
 
-ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const {
+ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const {
     const int32_t ikv = map_layer_ids.at(il);
 
     auto * v = layers[ikv].v;
 
+    const int64_t n_embd_v_gqa = v->ne[0];
     const int64_t n_tokens = v_cur->ne[2];
 
-    v_cur = ggml_reshape_2d(ctx, v_cur, hparams.n_embd_v_gqa(il), n_tokens);
+    v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
+
+    if (v_idxs && supports_set_rows) {
+        if (!v_trans) {
+            return ggml_set_rows(ctx, v, v_cur, v_idxs);
+        }
+
+        // the row becomes a single element
+        ggml_tensor * v_view = ggml_reshape_3d(ctx, v, 1, v->ne[1], v->ne[0]);
+
+        // note: the V cache is transposed when not using flash attention
+        v_cur = ggml_permute(ctx, ggml_reshape_3d(ctx, v_cur, v_cur->ne[0], 1, v_cur->ne[1]), 2, 0, 1, 3);
+
+        // note: we can be more explicit here at the cost of extra cont
+        //       however, above we take advantage that a row of single element is always continuous regardless of the row stride
+        //v_cur = ggml_transpose(ctx, v_cur);
+        //v_cur = ggml_cont_3d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1]);
+
+        // we broadcast the KV indices n_embd_v_gqa times
+        // v      [1,        n_kv,     n_embd_v_gqa]
+        // v_cur  [1,        n_tokens, n_embd_v_gqa]
+        // v_idxs [n_tokens, 1,        1]
+        return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
+    }
+
+    // TODO: fallback to old ggml_cpy() method for backwards compatibility
+    //       will be removed when ggml_set_rows() is adopted by all backends
 
     ggml_tensor * v_view = nullptr;
 
     if (!v_trans) {
         v_view = ggml_view_1d(ctx, v,
-                n_tokens*hparams.n_embd_v_gqa(il),
-                ggml_row_size(v->type, hparams.n_embd_v_gqa(il))*head_cur);
+                n_tokens*n_embd_v_gqa,
+                ggml_row_size(v->type, n_embd_v_gqa)*sinfo.head());
     } else {
-        // note: the V cache is transposed when not using flash attention
-        v_view = ggml_view_2d(ctx, v, n_tokens, hparams.n_embd_v_gqa(il),
-                (v->ne[1])*ggml_element_size(v),
-                (head_cur)*ggml_element_size(v));
-
         v_cur = ggml_transpose(ctx, v_cur);
+
+        v_view = ggml_view_2d(ctx, v, n_tokens, n_embd_v_gqa,
+                (v->ne[1]    )*ggml_element_size(v),
+                (sinfo.head())*ggml_element_size(v));
     }
 
     return ggml_cpy(ctx, v_cur, v_view);
 }
 
+ggml_tensor * llama_kv_cache_unified::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    const uint32_t n_tokens = ubatch.n_tokens;
+
+    ggml_tensor * k_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, n_tokens);
+
+    ggml_set_input(k_idxs);
+
+    return k_idxs;
+}
+
+ggml_tensor * llama_kv_cache_unified::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    const uint32_t n_tokens = ubatch.n_tokens;
+
+    ggml_tensor * v_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, n_tokens);
+
+    ggml_set_input(v_idxs);
+
+    return v_idxs;
+}
+
+void llama_kv_cache_unified::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
+    if (!supports_set_rows) {
+        return;
+    }
+
+    const uint32_t n_tokens = ubatch->n_tokens;
+
+    GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
+    int64_t * data = (int64_t *) dst->data;
+
+    for (int64_t i = 0; i < n_tokens; ++i) {
+        data[i] = sinfo.idxs.at(i);
+    }
+}
+
+void llama_kv_cache_unified::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
+    if (!supports_set_rows) {
+        return;
+    }
+
+    const uint32_t n_tokens = ubatch->n_tokens;
+
+    GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
+    int64_t * data = (int64_t *) dst->data;
+
+    for (int64_t i = 0; i < n_tokens; ++i) {
+        data[i] = sinfo.idxs.at(i);
+    }
+}
+
 void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
     const uint32_t n_tokens = ubatch->n_tokens;
 
@@ -1552,13 +1680,15 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
             ubatch.seq_id[i]   = &dest_seq_id;
         }
 
-        const auto head_cur = find_slot(ubatch);
-        if (head_cur < 0) {
+        const auto sinfo = find_slot(ubatch, true);
+        if (sinfo.empty()) {
             LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
             return false;
         }
 
-        apply_ubatch(head_cur, ubatch);
+        apply_ubatch(sinfo, ubatch);
+
+        const auto head_cur = sinfo.head();
 
         // keep the head at the old position because we will read the KV data into it in state_read_data()
         head = head_cur;
@@ -1744,7 +1874,11 @@ llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_stat
 llama_kv_cache_unified_context::llama_kv_cache_unified_context(
         llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
     n_kv = kv->get_size();
-    head = 0;
+
+    // create a dummy slot info - the actual data is irrelevant. we just need to build the graph
+    sinfos.resize(1);
+    sinfos[0].idxs.resize(1);
+    sinfos[0].idxs[0] = 0;
 }
 
 llama_kv_cache_unified_context::llama_kv_cache_unified_context(
@@ -1759,8 +1893,8 @@ llama_kv_cache_unified_context::llama_kv_cache_unified_context(
 
 llama_kv_cache_unified_context::llama_kv_cache_unified_context(
         llama_kv_cache_unified * kv,
-        llama_kv_cache_unified::ubatch_heads heads,
-        std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) {
+        llama_kv_cache_unified::slot_info_vec_t sinfos,
+        std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), sinfos(std::move(sinfos)), ubatches(std::move(ubatches)) {
 }
 
 llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
@@ -1768,7 +1902,7 @@ llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
 bool llama_kv_cache_unified_context::next() {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
-    if (++i_next >= ubatches.size()) {
+    if (++i_cur >= ubatches.size()) {
         return false;
     }
 
@@ -1785,10 +1919,9 @@ bool llama_kv_cache_unified_context::apply() {
         return true;
     }
 
-    kv->apply_ubatch(heads[i_next], ubatches[i_next]);
+    kv->apply_ubatch(sinfos[i_cur], ubatches[i_cur]);
 
     n_kv = kv->get_n_kv();
-    head = heads[i_next];
 
     return true;
 }
@@ -1800,7 +1933,7 @@ llama_memory_status llama_kv_cache_unified_context::get_status() const {
 const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
     assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
 
-    return ubatches[i_next];
+    return ubatches[i_cur];
 }
 
 uint32_t llama_kv_cache_unified_context::get_n_kv() const {
@@ -1815,18 +1948,34 @@ ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t
     return kv->get_v(ctx, il, n_kv);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
-    return kv->cpy_k(ctx, k_cur, il, head);
+ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
+    return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
+}
+
+ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const {
+    return kv->cpy_v(ctx, v_cur, v_idxs, il, sinfos[i_cur]);
+}
+
+ggml_tensor * llama_kv_cache_unified_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    return kv->build_input_k_idxs(ctx, ubatch);
 }
 
-ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
-    return kv->cpy_v(ctx, v_cur, il, head);
+ggml_tensor * llama_kv_cache_unified_context::build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    return kv->build_input_v_idxs(ctx, ubatch);
 }
 
 void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
     kv->set_input_k_shift(dst);
 }
 
+void llama_kv_cache_unified_context::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+    kv->set_input_k_idxs(dst, ubatch, sinfos[i_cur]);
+}
+
+void llama_kv_cache_unified_context::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const {
+    kv->set_input_v_idxs(dst, ubatch, sinfos[i_cur]);
+}
+
 void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
     kv->set_input_kq_mask(dst, ubatch, causal_attn);
 }

examples/talk-llama/llama-kv-cache-unified.h

@@ -24,8 +24,6 @@ public:
     // this callback is used to filter out layers that should not be included in the cache
     using layer_filter_cb = std::function<bool(int32_t il)>;
 
-    using ubatch_heads = std::vector<uint32_t>;
-
     struct defrag_info {
         bool empty() const {
             return ids.empty();
@@ -37,6 +35,32 @@ public:
         std::vector<uint32_t> ids;
     };
 
+    // for each ubatch, create a slot_info that contains information about where the ubatch should be inserted in the
+    //   KV cells. for example, cell indices for each token, such that: token[i] -> goes to cells[idxs[i]]
+    struct slot_info {
+        // data for ggml_set_rows
+        using idx_vec_t = std::vector<uint32_t>;
+
+        idx_vec_t idxs;
+
+        uint32_t head() const {
+            return idxs.at(0);
+        }
+
+        bool empty() const {
+            return idxs.empty();
+        }
+
+        void clear() {
+            idxs.clear();
+        }
+
+        // TODO: implement
+        //std::vector<idx_vec_t> seq_idxs;
+    };
+
+    using slot_info_vec_t = std::vector<slot_info>;
+
     llama_kv_cache_unified(
             const llama_model &  model,
               layer_filter_cb && filter,
@@ -102,30 +126,37 @@ public:
     ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv) const;
 
     // store k_cur and v_cur in the cache based on the provided head location
-    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il, uint32_t head_cur) const;
-    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il, uint32_t head_cur) const;
+    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const;
+    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const;
 
     //
     // preparation API
     //
 
-    // find places for the provided ubatches in the cache, returns the head locations
+    // find places for the provided ubatches in the cache, returns the slot infos
     // return empty vector on failure
-    ubatch_heads prepare(const std::vector<llama_ubatch> & ubatches);
+    slot_info_vec_t prepare(const std::vector<llama_ubatch> & ubatches);
 
     bool update(llama_context * lctx, bool do_shift, const defrag_info & dinfo);
 
-    // return the cell position where we can insert the ubatch
-    // return -1 on failure to find a contiguous slot of kv cells
-    int32_t find_slot(const llama_ubatch & ubatch) const;
+    // find a slot of kv cells that can hold the ubatch
+    // if cont == true, then the slot must be continuous
+    // return empty slot_info on failure
+    slot_info find_slot(const llama_ubatch & ubatch, bool cont) const;
 
-    // emplace the ubatch context into slot: [head_cur, head_cur + ubatch.n_tokens)
-    void apply_ubatch(uint32_t head_cur, const llama_ubatch & ubatch);
+    // emplace the ubatch context into slot: [sinfo.idxs[0...ubatch.n_tokens - 1]]
+    void apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch);
 
     //
-    // set_input API
+    // input API
     //
 
+    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+    ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+
+    void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
+    void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;
+
     void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
     void set_input_k_shift   (ggml_tensor * dst) const;
     void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
@@ -157,8 +188,13 @@ private:
     // SWA
     const uint32_t n_swa = 0;
 
+    // env: LLAMA_KV_CACHE_DEBUG
     int debug = 0;
 
+    // env: LLAMA_SET_ROWS (temporary)
+    // ref: https://github.com/ggml-org/llama.cpp/pull/14285
+    int supports_set_rows = false;
+
     const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
 
     std::vector<ggml_context_ptr>        ctxs;
@@ -211,8 +247,8 @@ private:
 class llama_kv_cache_unified_context : public llama_memory_context_i {
 public:
     // some shorthands
-    using ubatch_heads = llama_kv_cache_unified::ubatch_heads;
-    using defrag_info  = llama_kv_cache_unified::defrag_info;
+    using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
+    using defrag_info     = llama_kv_cache_unified::defrag_info;
 
     // used for errors
     llama_kv_cache_unified_context(llama_memory_status status);
@@ -231,7 +267,7 @@ public:
     // used to create a batch procesing context from a batch
     llama_kv_cache_unified_context(
             llama_kv_cache_unified * kv,
-            ubatch_heads heads,
+            slot_info_vec_t sinfos,
             std::vector<llama_ubatch> ubatches);
 
     virtual ~llama_kv_cache_unified_context();
@@ -257,11 +293,16 @@ public:
     ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
 
     // store k_cur and v_cur in the cache based on the provided head location
-    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const;
-    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const;
+    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;
+    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const;
+
+    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+    ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
 
-    void set_input_k_shift(ggml_tensor * dst) const;
+    void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;
+    void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;
 
+    void set_input_k_shift   (ggml_tensor * dst) const;
     void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
     void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;
 
@@ -283,10 +324,10 @@ private:
     // batch processing context
     //
 
-    // the index of the next ubatch to process
-    size_t i_next = 0;
+    // the index of the cur ubatch to process
+    size_t i_cur = 0;
 
-    ubatch_heads heads;
+    slot_info_vec_t sinfos;
 
     std::vector<llama_ubatch> ubatches;
 
@@ -297,7 +338,4 @@ private:
     // a heuristic, to avoid attending the full cache if it is not yet utilized
     // as the cache gets filled, the benefit from this heuristic disappears
     int32_t n_kv;
-
-    // the beginning of the current slot in which the ubatch will be inserted
-    int32_t head;
 };

examples/talk-llama/llama-kv-cells.h

@@ -105,10 +105,30 @@ public:
         res.resize(n);
 
         for (uint32_t j = 0; j < n; ++j) {
-            res.pos[j] = pos[i + j];
-            res.seq[j] = seq[i + j];
+            const auto idx = i + j;
 
-            assert(shift[i + j] == 0);
+            res.pos[j] = pos[idx];
+            res.seq[j] = seq[idx];
+
+            assert(shift[idx] == 0);
+        }
+
+        return res;
+    }
+
+    // copy the state of cells [idxs[0], idxs[1], ..., idxs[idxs.size() - 1])
+    llama_kv_cells_unified cp(const std::vector<uint32_t> & idxs) const {
+        llama_kv_cells_unified res;
+
+        res.resize(idxs.size());
+
+        for (uint32_t j = 0; j < idxs.size(); ++j) {
+            const auto idx = idxs[j];
+
+            res.pos[j] = pos[idx];
+            res.seq[j] = seq[idx];
+
+            assert(shift[idx] == 0);
         }
 
         return res;
@@ -119,26 +139,58 @@ public:
         assert(i + other.pos.size() <= pos.size());
 
         for (uint32_t j = 0; j < other.pos.size(); ++j) {
-            if (pos[i + j] == -1 && other.pos[j] != -1) {
+            const auto idx = i + j;
+
+            if (pos[idx] == -1 && other.pos[j] != -1) {
                 used.insert(i + j);
             }
 
-            if (pos[i + j] != -1 && other.pos[j] == -1) {
+            if (pos[idx] != -1 && other.pos[j] == -1) {
                 used.erase(i + j);
             }
 
-            if (pos[i + j] != -1) {
+            if (pos[idx] != -1) {
                 seq_pos_rm(i + j);
             }
 
-            pos[i + j] = other.pos[j];
-            seq[i + j] = other.seq[j];
+            pos[idx] = other.pos[j];
+            seq[idx] = other.seq[j];
 
-            if (pos[i + j] != -1) {
+            if (pos[idx] != -1) {
                 seq_pos_add(i + j);
             }
 
-            assert(shift[i + j] == 0);
+            assert(shift[idx] == 0);
+        }
+    }
+
+    // set the state of cells [idxs[0], idxs[1], ..., idxs[idxs.size() - 1])
+    void set(const std::vector<uint32_t> & idxs, const llama_kv_cells_unified & other) {
+        assert(idxs.size() == other.pos.size());
+
+        for (uint32_t j = 0; j < other.pos.size(); ++j) {
+            const auto idx = idxs[j];
+
+            if (pos[idx] == -1 && other.pos[j] != -1) {
+                used.insert(idx);
+            }
+
+            if (pos[idx] != -1 && other.pos[j] == -1) {
+                used.erase(idx);
+            }
+
+            if (pos[idx] != -1) {
+                seq_pos_rm(idx);
+            }
+
+            pos[idx] = other.pos[j];
+            seq[idx] = other.seq[j];
+
+            if (pos[idx] != -1) {
+                seq_pos_add(idx);
+            }
+
+            assert(shift[idx] == 0);
         }
     }
 

examples/talk-llama/llama-memory-hybrid.cpp

@@ -70,7 +70,7 @@ 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);
+                ubatch = balloc.split_equal(n_ubatch, false);
             }
 
             if (ubatch.n_tokens == 0) {
@@ -80,6 +80,11 @@ llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ba
             ubatches.push_back(std::move(ubatch)); // NOLINT
         }
 
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
         // prepare the recurrent batches first
         if (!mem_recr->prepare(ubatches)) {
             // TODO: will the recurrent cache be in an undefined context at this point?
@@ -195,11 +200,11 @@ llama_memory_hybrid_context::llama_memory_hybrid_context(
 
 llama_memory_hybrid_context::llama_memory_hybrid_context(
               llama_memory_hybrid * mem,
-            std::vector<uint32_t>   heads_attn,
+                  slot_info_vec_t   sinfos_attn,
         std::vector<llama_ubatch>   ubatches) :
     ubatches(std::move(ubatches)),
     // note: here we copy the ubatches. not sure if this is ideal
-    ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
+    ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
     ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(),                        this->ubatches)),
     status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
 }

examples/talk-llama/llama-memory-hybrid.h

@@ -92,6 +92,8 @@ private:
 
 class llama_memory_hybrid_context : public llama_memory_context_i {
 public:
+    using slot_info_vec_t = llama_kv_cache_unified::slot_info_vec_t;
+
     // init failure
     explicit llama_memory_hybrid_context(llama_memory_status status);
 
@@ -107,7 +109,7 @@ public:
     // init success
     llama_memory_hybrid_context(
               llama_memory_hybrid * mem,
-            std::vector<uint32_t>   heads_attn,
+                  slot_info_vec_t   sinfos_attn,
         std::vector<llama_ubatch>   ubatches);
 
     ~llama_memory_hybrid_context() = default;

examples/talk-llama/llama-memory-recurrent.cpp

@@ -25,9 +25,6 @@ llama_memory_recurrent::llama_memory_recurrent(
                  uint32_t    n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
     const int32_t n_layer = hparams.n_layer;
 
-    LLAMA_LOG_INFO("%s: mem_size = %u, n_seq_max = %u, type_r = '%s', type_s = '%s', n_layer = %d\n",
-            __func__, mem_size, n_seq_max, ggml_type_name(type_r), ggml_type_name(type_s), n_layer);
-
     head = 0;
     size = mem_size;
     used = 0;
@@ -84,7 +81,7 @@ llama_memory_recurrent::llama_memory_recurrent(
 
         ggml_context * ctx = ctx_for_buft(buft);
         if (!ctx) {
-            throw std::runtime_error("failed to create ggml context for kv cache");
+            throw std::runtime_error("failed to create ggml context for rs cache");
         }
 
         ggml_tensor * r = ggml_new_tensor_1d(ctx, type_r, hparams.n_embd_r()*mem_size);
@@ -102,10 +99,10 @@ llama_memory_recurrent::llama_memory_recurrent(
 
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
         if (!buf) {
-            throw std::runtime_error("failed to allocate buffer for kv cache");
+            throw std::runtime_error("failed to allocate buffer for rs cache");
         }
         ggml_backend_buffer_clear(buf, 0);
-        LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+        LLAMA_LOG_INFO("%s: %10s RS buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
         bufs.emplace_back(buf);
     }
 
@@ -113,8 +110,8 @@ llama_memory_recurrent::llama_memory_recurrent(
         const size_t memory_size_r = size_r_bytes();
         const size_t memory_size_s = size_s_bytes();
 
-        LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, R (%s): %7.2f MiB, S (%s): %7.2f MiB\n", __func__,
-                (float)(memory_size_r + memory_size_s) / (1024.0f * 1024.0f),
+        LLAMA_LOG_INFO("%s: size = %7.2f MiB (%6u cells, %3d layers, %2u seqs), R (%s): %7.2f MiB, S (%s): %7.2f MiB\n", __func__,
+                (float)(memory_size_r + memory_size_s) / (1024.0f * 1024.0f), mem_size, n_layer, n_seq_max,
                 ggml_type_name(type_r), (float)memory_size_r / (1024.0f * 1024.0f),
                 ggml_type_name(type_s), (float)memory_size_s / (1024.0f * 1024.0f));
     }
@@ -374,7 +371,7 @@ 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);
+                ubatch = balloc.split_equal(n_ubatch, false);
             }
 
             if (ubatch.n_tokens == 0) {
@@ -384,6 +381,11 @@ llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr &
             ubatches.push_back(std::move(ubatch)); // NOLINT
         }
 
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
         if (!prepare(ubatches)) {
             break;
         }

examples/talk-llama/llama-model.h

@@ -32,17 +32,21 @@ enum llm_type {
     LLM_TYPE_190M,
     LLM_TYPE_220M,
     LLM_TYPE_250M,
+    LLM_TYPE_256M,
     LLM_TYPE_270M,
     LLM_TYPE_335M,
+    LLM_TYPE_350M,
     LLM_TYPE_410M,
     LLM_TYPE_450M,
     LLM_TYPE_475M,
+    LLM_TYPE_700M,
     LLM_TYPE_770M,
     LLM_TYPE_780M,
     LLM_TYPE_0_3B,
     LLM_TYPE_0_5B,
     LLM_TYPE_0_6B,
     LLM_TYPE_1B,
+    LLM_TYPE_1_2B,
     LLM_TYPE_1_3B,
     LLM_TYPE_1_4B,
     LLM_TYPE_1_5B,
@@ -94,6 +98,7 @@ enum llm_type {
     LLM_TYPE_57B_A14B,
     LLM_TYPE_17B_16E, // llama4 Scout
     LLM_TYPE_17B_128E, // llama4 Maverick
+    LLM_TYPE_A13B,
     LLM_TYPE_30B_A3B,
     LLM_TYPE_235B_A22B,
     LLM_TYPE_E2B,
@@ -153,6 +158,12 @@ struct llama_layer_convnext {
     struct ggml_tensor * gamma = nullptr;
 };
 
+struct llama_layer_shortconv {
+    struct ggml_tensor * in_proj  = nullptr;
+    struct ggml_tensor * conv     = nullptr;
+    struct ggml_tensor * out_proj = nullptr;
+};
+
 struct llama_layer {
     // normalization
     struct ggml_tensor * attn_norm       = nullptr;
@@ -172,6 +183,10 @@ struct llama_layer {
     struct ggml_tensor * ffn_sub_norm    = nullptr;
     struct ggml_tensor * attn_norm_cross = nullptr;
     struct ggml_tensor * attn_norm_enc   = nullptr;
+    struct ggml_tensor * ssm_norm        = nullptr;
+    struct ggml_tensor * ssm_dt_norm     = nullptr;
+    struct ggml_tensor * ssm_b_norm      = nullptr;
+    struct ggml_tensor * ssm_c_norm      = nullptr;
 
     // attention
     struct ggml_tensor * wq        = nullptr;
@@ -335,6 +350,8 @@ struct llama_layer {
     struct llama_layer_posnet posnet;
 
     struct llama_layer_convnext convnext;
+
+    struct llama_layer_shortconv shortconv;
 };
 
 struct llama_model {

examples/talk-llama/llama-quant.cpp

@@ -844,6 +844,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         // do not quantize Mamba's small yet 2D weights
         // NOTE: can't use LLM_TN here because the layer number is not known
         quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
+        quantize &= name.find("shortconv.conv.weight") == std::string::npos;
 
         // do not quantize RWKV's small yet 2D weights
         quantize &= name.find("time_mix_first.weight") == std::string::npos;

examples/talk-llama/llama-vocab.cpp

@@ -351,6 +351,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                 break;
             case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
             case LLAMA_VOCAB_PRE_TYPE_QWEN2:
+            case LLAMA_VOCAB_PRE_TYPE_HUNYUAN:
                 regex_exprs = {
                     // original regex from tokenizer.json
                     // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
@@ -1522,7 +1523,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "llama-v3" ||
                     tokenizer_pre == "llama-bpe"||
                     tokenizer_pre == "falcon3"  ||
-                    tokenizer_pre == "pixtral") {
+                    tokenizer_pre == "falcon-h1" ||
+                    tokenizer_pre == "pixtral"  ||
+                    tokenizer_pre == "midm-2.0" ||
+                    tokenizer_pre == "lfm2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
                 ignore_merges = true;
                 add_bos = true;
@@ -1554,7 +1558,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "jina-de" ||
                     tokenizer_pre == "gigachat"   ||
                     tokenizer_pre == "jina-v2-es" ||
-                    tokenizer_pre == "jina-v2-de") {
+                    tokenizer_pre == "jina-v2-de" ||
+                    tokenizer_pre == "a.x-4.0") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
             } else if (
                     tokenizer_pre == "jina-v1-en" ||
@@ -1656,6 +1661,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "seed-coder") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_SEED_CODER;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "hunyuan") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN;
+                clean_spaces = false;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -1839,6 +1848,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<EOT>"
                         || t.first == "_<EOT>"
                         || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
+                        || t.first == "<end_of_utterance>" // smoldocling
                    ) {
                     special_eot_id = t.second;
                     if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -1998,6 +2008,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     || t.first == "<EOT>"
                     || t.first == "_<EOT>"
                     || t.first == "<|end_of_text|>"
+                    || t.first == "<end_of_utterance>" // smoldocling
                ) {
                 special_eog_ids.insert(t.second);
                 if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {

examples/talk-llama/llama-vocab.h

@@ -6,6 +6,47 @@
 #include <vector>
 #include <memory>
 
+// 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,
+};
+
 struct LLM_KV;
 struct llama_model_loader;
 

examples/talk-llama/llama.h

@@ -79,46 +79,6 @@ extern "C" {
         LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
     };
 
-    // 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,
-    };
-
     enum llama_rope_type {
         LLAMA_ROPE_TYPE_NONE   = -1,
         LLAMA_ROPE_TYPE_NORM   = 0,