Update Submodule vendor/llama.cpp 563dec8..1c3c967

Signed-off-by: JamePeng <jame_peng@sina.com>

Author: JamePeng

llama_cpp/_internals.py

@@ -2064,6 +2064,7 @@ def _build_sampler_chain(self):
         # Note: In some implementations, penalties come before other samplers
         if CommonSamplerType.PENALTIES in p.samplers:
             s.add_penalties(
+                self.n_vocab,
                 p.penalty_last_n,
                 p.penalty_repeat,
                 p.penalty_freq,
@@ -3176,8 +3177,8 @@ def add_grammar(
                 c_trigger_tokens, len(trigger_tokens)
             ))
 
-    def add_penalties(self, penalty_last_n: int, penalty_repeat: float, penalty_freq: float, penalty_present: float):
-        self._add_sampler(llama_cpp.llama_sampler_init_penalties(penalty_last_n, penalty_repeat, penalty_freq, penalty_present))
+    def add_penalties(self, n_vocab: int, penalty_last_n: int, penalty_repeat: float, penalty_freq: float, penalty_present: float):
+        self._add_sampler(llama_cpp.llama_sampler_init_penalties(n_vocab, penalty_last_n, penalty_repeat, penalty_freq, penalty_present))
 
     def add_dry(self, model: LlamaModel, multiplier: float, base: float, allowed_len: int, last_n: int, breakers: List[str]):
         """DRY (Don't Repeat Yourself) sampler."""

llama_cpp/llama_cpp.py

@@ -146,60 +146,63 @@ class llama_vocab_type(enum.IntEnum):
 # https://github.com/ggml-org/llama.cpp/blob/master/src/llama-vocab.h#L10
 # // pre-tokenization types
 # enum llama_vocab_pre_type {
-#     LLAMA_VOCAB_PRE_TYPE_DEFAULT         = 0,
-#     LLAMA_VOCAB_PRE_TYPE_LLAMA3          = 1,
-#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM    = 2,
-#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER  = 3,
-#     LLAMA_VOCAB_PRE_TYPE_FALCON          = 4,
-#     LLAMA_VOCAB_PRE_TYPE_MPT             = 5,
-#     LLAMA_VOCAB_PRE_TYPE_STARCODER       = 6,
-#     LLAMA_VOCAB_PRE_TYPE_GPT2            = 7,
-#     LLAMA_VOCAB_PRE_TYPE_REFACT          = 8,
-#     LLAMA_VOCAB_PRE_TYPE_COMMAND_R       = 9,
-#     LLAMA_VOCAB_PRE_TYPE_STABLELM2       = 10,
-#     LLAMA_VOCAB_PRE_TYPE_QWEN2           = 11,
-#     LLAMA_VOCAB_PRE_TYPE_OLMO            = 12,
-#     LLAMA_VOCAB_PRE_TYPE_DBRX            = 13,
-#     LLAMA_VOCAB_PRE_TYPE_SMAUG           = 14,
-#     LLAMA_VOCAB_PRE_TYPE_PORO            = 15,
-#     LLAMA_VOCAB_PRE_TYPE_CHATGLM3        = 16,
-#     LLAMA_VOCAB_PRE_TYPE_CHATGLM4        = 17,
-#     LLAMA_VOCAB_PRE_TYPE_VIKING          = 18,
-#     LLAMA_VOCAB_PRE_TYPE_JAIS            = 19,
-#     LLAMA_VOCAB_PRE_TYPE_TEKKEN          = 20,
-#     LLAMA_VOCAB_PRE_TYPE_SMOLLM          = 21,
-#     LLAMA_VOCAB_PRE_TYPE_CODESHELL       = 22,
-#     LLAMA_VOCAB_PRE_TYPE_BLOOM           = 23,
-#     LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH    = 24,
-#     LLAMA_VOCAB_PRE_TYPE_EXAONE          = 25,
-#     LLAMA_VOCAB_PRE_TYPE_CHAMELEON       = 26,
-#     LLAMA_VOCAB_PRE_TYPE_MINERVA         = 27,
-#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM   = 28,
-#     LLAMA_VOCAB_PRE_TYPE_GPT4O           = 29,
-#     LLAMA_VOCAB_PRE_TYPE_SUPERBPE        = 30,
-#     LLAMA_VOCAB_PRE_TYPE_TRILLION        = 31,
-#     LLAMA_VOCAB_PRE_TYPE_BAILINGMOE      = 32,
-#     LLAMA_VOCAB_PRE_TYPE_LLAMA4          = 33,
-#     LLAMA_VOCAB_PRE_TYPE_PIXTRAL         = 34,
-#     LLAMA_VOCAB_PRE_TYPE_SEED_CODER      = 35,
-#     LLAMA_VOCAB_PRE_TYPE_HUNYUAN         = 36,
-#     LLAMA_VOCAB_PRE_TYPE_KIMI_K2         = 37,
-#     LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE   = 38,
-#     LLAMA_VOCAB_PRE_TYPE_GROK_2          = 39,
-#     LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
-#     LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2      = 41,
-#     LLAMA_VOCAB_PRE_TYPE_AFMOE           = 42,
-#     LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN      = 43,
-#     LLAMA_VOCAB_PRE_TYPE_YOUTU           = 44,
-#     LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE      = 45,
-#     LLAMA_VOCAB_PRE_TYPE_QWEN35          = 46,
-#     LLAMA_VOCAB_PRE_TYPE_TINY_AYA        = 47,
-#     LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM       = 48,
-#     LLAMA_VOCAB_PRE_TYPE_JAIS2           = 49,
-#     LLAMA_VOCAB_PRE_TYPE_GEMMA4          = 50,
-#     LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE      = 51,
-#     LLAMA_VOCAB_PRE_TYPE_MINICPM5        = 52,
-#     LLAMA_VOCAB_PRE_TYPE_WHITESPACE      = 53,
+#     LLAMA_VOCAB_PRE_TYPE_DEFAULT           = 0,
+#     LLAMA_VOCAB_PRE_TYPE_LLAMA3            = 1,
+#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM      = 2,
+#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER    = 3,
+#     LLAMA_VOCAB_PRE_TYPE_FALCON            = 4,
+#     LLAMA_VOCAB_PRE_TYPE_MPT               = 5,
+#     LLAMA_VOCAB_PRE_TYPE_STARCODER         = 6,
+#     LLAMA_VOCAB_PRE_TYPE_GPT2              = 7,
+#     LLAMA_VOCAB_PRE_TYPE_REFACT            = 8,
+#     LLAMA_VOCAB_PRE_TYPE_COMMAND_R         = 9,
+#     LLAMA_VOCAB_PRE_TYPE_STABLELM2         = 10,
+#     LLAMA_VOCAB_PRE_TYPE_QWEN2             = 11,
+#     LLAMA_VOCAB_PRE_TYPE_OLMO              = 12,
+#     LLAMA_VOCAB_PRE_TYPE_DBRX              = 13,
+#     LLAMA_VOCAB_PRE_TYPE_SMAUG             = 14,
+#     LLAMA_VOCAB_PRE_TYPE_PORO              = 15,
+#     LLAMA_VOCAB_PRE_TYPE_CHATGLM3          = 16,
+#     LLAMA_VOCAB_PRE_TYPE_CHATGLM4          = 17,
+#     LLAMA_VOCAB_PRE_TYPE_VIKING            = 18,
+#     LLAMA_VOCAB_PRE_TYPE_JAIS              = 19,
+#     LLAMA_VOCAB_PRE_TYPE_TEKKEN            = 20,
+#     LLAMA_VOCAB_PRE_TYPE_SMOLLM            = 21,
+#     LLAMA_VOCAB_PRE_TYPE_CODESHELL         = 22,
+#     LLAMA_VOCAB_PRE_TYPE_BLOOM             = 23,
+#     LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH      = 24,
+#     LLAMA_VOCAB_PRE_TYPE_EXAONE            = 25,
+#     LLAMA_VOCAB_PRE_TYPE_CHAMELEON         = 26,
+#     LLAMA_VOCAB_PRE_TYPE_MINERVA           = 27,
+#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM     = 28,
+#     LLAMA_VOCAB_PRE_TYPE_GPT4O             = 29,
+#     LLAMA_VOCAB_PRE_TYPE_SUPERBPE          = 30,
+#     LLAMA_VOCAB_PRE_TYPE_TRILLION          = 31,
+#     LLAMA_VOCAB_PRE_TYPE_BAILINGMOE        = 32,
+#     LLAMA_VOCAB_PRE_TYPE_LLAMA4            = 33,
+#     LLAMA_VOCAB_PRE_TYPE_PIXTRAL           = 34,
+#     LLAMA_VOCAB_PRE_TYPE_SEED_CODER        = 35,
+#     LLAMA_VOCAB_PRE_TYPE_HUNYUAN           = 36,
+#     LLAMA_VOCAB_PRE_TYPE_KIMI_K2           = 37,
+#     LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE     = 38,
+#     LLAMA_VOCAB_PRE_TYPE_GROK_2            = 39,
+#     LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING   = 40,
+#     LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2        = 41,
+#     LLAMA_VOCAB_PRE_TYPE_AFMOE             = 42,
+#     LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN        = 43,
+#     LLAMA_VOCAB_PRE_TYPE_YOUTU             = 44,
+#     LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE        = 45,
+#     LLAMA_VOCAB_PRE_TYPE_QWEN35            = 46,
+#     LLAMA_VOCAB_PRE_TYPE_TINY_AYA          = 47,
+#     LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM         = 48,
+#     LLAMA_VOCAB_PRE_TYPE_JAIS2             = 49,
+#     LLAMA_VOCAB_PRE_TYPE_GEMMA4            = 50,
+#     LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE        = 51,
+#     LLAMA_VOCAB_PRE_TYPE_MINICPM5          = 52,
+#     LLAMA_VOCAB_PRE_TYPE_WHITESPACE        = 53,
+#     LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54,
+#     LLAMA_VOCAB_PRE_TYPE_MELLUM2           = 55,
+#     LLAMA_VOCAB_PRE_TYPE_LAGUNA            = 56,
 # };
 class llama_vocab_pre_type(enum.IntEnum):
     LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0
@@ -256,6 +259,9 @@ class llama_vocab_pre_type(enum.IntEnum):
     LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE = 51
     LLAMA_VOCAB_PRE_TYPE_MINICPM5 = 52
     LLAMA_VOCAB_PRE_TYPE_WHITESPACE = 53
+    LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54
+    LLAMA_VOCAB_PRE_TYPE_MELLUM2 = 55
+    LLAMA_VOCAB_PRE_TYPE_LAGUNA = 56
 
 
 # // note: these values should be synchronized with ggml_rope
@@ -4130,22 +4136,19 @@ def llama_chat_builtin_templates(
 #     struct ggml_tensor * probs;
 #     struct ggml_tensor * sampled;
 #     struct ggml_tensor * candidates;
-#     int64_t              n_vocab;
 # };
 class llama_sampler_data(ctypes.Structure):
     if TYPE_CHECKING:
         logits: ctypes.c_void_p
         probs: ctypes.c_void_p
         sampled: ctypes.c_void_p
         candidates: ctypes.c_void_p
-        n_vocab: ctypes.c_int64
 
     _fields_ = [
         ("logits", ctypes.c_void_p),
         ("probs", ctypes.c_void_p),
         ("sampled", ctypes.c_void_p),
         ("candidates", ctypes.c_void_p),
-        ("n_vocab", ctypes.c_int64),
     ]
 
 
@@ -4656,16 +4659,24 @@ def llama_sampler_init_grammar_lazy_patterns(
 
 # /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
 # LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
+#                          int32_t   n_vocab,
 #                          int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
 #                            float   penalty_repeat,   // must be > 0.0, 1.0 = disabled
 #                            float   penalty_freq,     // must be finite, 0.0 = disabled
 #                            float   penalty_present); // must be finite, 0.0 = disabled
 @ctypes_function(
     "llama_sampler_init_penalties",
-    [ctypes.c_int32, ctypes.c_float, ctypes.c_float, ctypes.c_float],
+    [
+        ctypes.c_int32,
+        ctypes.c_int32,
+        ctypes.c_float,
+        ctypes.c_float,
+        ctypes.c_float,
+    ],
     llama_sampler_p_ctypes,
 )
 def llama_sampler_init_penalties(
+    n_vocab: int,
     penalty_last_n: int,
     penalty_repeat: float,
     penalty_freq: float,