Merge https://github.com/abetlen/llama-cpp-python

llama_cpp/llama.py

@@ -79,6 +79,7 @@ class Llama:
         n_threads: Optional[int] = None,
         n_threads_batch: Optional[int] = None,
         rope_scaling_type: Optional[int] = llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
+        pooling_type: int = llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED,
         rope_freq_base: float = 0.0,
         rope_freq_scale: float = 0.0,
         yarn_ext_factor: float = -1.0,
@@ -151,6 +152,7 @@ class Llama:
             n_threads: Number of threads to use for generation
             n_threads_batch: Number of threads to use for batch processing
             rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054
+            pooling_type: Pooling type, from `enum llama_pooling_type`.
             rope_freq_base: RoPE base frequency, 0 = from model
             rope_freq_scale: RoPE frequency scaling factor, 0 = from model
             yarn_ext_factor: YaRN extrapolation mix factor, negative = from model
@@ -271,6 +273,7 @@ class Llama:
             if rope_scaling_type is not None
             else llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED
         )
+        self.context_params.pooling_type = pooling_type
         self.context_params.rope_freq_base = (
             rope_freq_base if rope_freq_base != 0.0 else 0
         )
@@ -814,9 +817,12 @@ class Llama:
 
             # store embeddings
             for i in range(n_seq):
-                embedding: List[float] = llama_cpp.llama_get_embeddings_seq(
+                ptr = llama_cpp.llama_get_embeddings_seq(
                     self._ctx.ctx, i
-                )[:n_embd]
+                )
+                if not ptr:
+                    raise RuntimeError("Failed to get embeddings from sequence pooling type is not set")
+                embedding: List[float] = ptr[:n_embd]
                 if normalize:
                     norm = float(np.linalg.norm(embedding))
                     embedding = [v / norm for v in embedding]

llama_cpp/llama_chat_format.py

@@ -339,16 +339,7 @@ def chat_formatter_to_chat_completion_handler(
             stop = stop + rstop
 
         if response_format is not None and response_format["type"] == "json_object":
-            try:
-                # create grammar from json schema
-                if "schema" in response_format:
-                    grammar = llama_grammar.LlamaGrammar.from_json_schema(
-                        json.dumps(response_format["schema"]), verbose=llama.verbose
-                    )
-            except Exception as e:
-                grammar = llama_grammar.LlamaGrammar.from_string(
-                    llama_grammar.JSON_GBNF, verbose=llama.verbose
-                )
+            grammar = _grammar_for_response_format(response_format, verbose=llama.verbose)
 
         completion_or_chunks = llama.create_completion(
             prompt=prompt,
@@ -606,6 +597,35 @@ def _format_chatglm3(
             ret += role
     return ret
 
+def _grammar_for_json(verbose:bool=False):
+    return llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF, verbose=verbose)
+
+def _grammar_for_json_schema(
+        schema: str,
+        verbose: bool = False,
+        fallback_to_json: bool = True
+):
+    try:
+        return llama_grammar.LlamaGrammar.from_json_schema(schema, verbose=verbose)
+    except Exception as e:
+        if fallback_to_json:
+            return _grammar_for_json(verbose=verbose)
+        else:
+            raise e
+
+def _grammar_for_response_format(
+        response_format: llama_types.ChatCompletionRequestResponseFormat,
+        verbose: bool = False
+):
+    if response_format["type"] != "json_object":
+        return None
+
+    if "schema" in response_format:
+        return _grammar_for_json_schema(
+            json.dumps(response_format["schema"]), verbose=verbose
+        )
+    else:
+        return _grammar_for_json(verbose=verbose)
 
 ### Chat Formats ###
 
@@ -1994,16 +2014,7 @@ class Llava15ChatHandler:
         prompt = llama.input_ids[: llama.n_tokens].tolist()
 
         if response_format is not None and response_format["type"] == "json_object":
-            try:
-                # create grammar from json schema
-                if "schema" in response_format:
-                    grammar = llama_grammar.LlamaGrammar.from_json_schema(
-                        json.dumps(response_format["schema"])
-                    )
-            except Exception as e:
-                grammar = llama_grammar.LlamaGrammar.from_string(
-                    llama_grammar.JSON_GBNF
-                )
+            grammar = _grammar_for_response_format(response_format)
 
         return _convert_completion_to_chat(
             llama.create_completion(
@@ -2159,26 +2170,10 @@ def chatml_function_calling(
             tool_calls=None,
             add_generation_prompt=True,
         )
+
         if response_format is not None and response_format["type"] == "json_object":
-            try:
-                grammar = (
-                    llama_grammar.LlamaGrammar.from_json_schema(
-                        json.dumps(response_format["schema"])
-                    )
-                    if "schema" in response_format
-                    else None
-                )
-            except Exception as e:
-                if llama.verbose:
-                    print(
-                        "Failed to parse response format as JSON schema, falling back to default grammar"
-                    )
-                    print(e)
-            grammar = (
-                llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
-                if grammar is None
-                else grammar
-            )
+            grammar = _grammar_for_response_format(response_format)
+
         return _convert_completion_to_chat(
             llama.create_completion(
                 prompt=prompt,

llama_cpp/llama_cpp.py

@@ -198,13 +198,15 @@ llama_seq_id = ctypes.c_int32
 
 
 # enum llama_vocab_type {
-#     LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
-#     LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
-#     LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
+#     LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
+#     LLAMA_VOCAB_TYPE_SPM  = 1, // SentencePiece
+#     LLAMA_VOCAB_TYPE_BPE  = 2, // Byte Pair Encoding
+#     LLAMA_VOCAB_TYPE_WPM  = 3, // WordPiece
 # };
-LLAMA_VOCAB_TYPE_SPM = 0
-LLAMA_VOCAB_TYPE_BPE = 1
-LLAMA_VOCAB_TYPE_WPM = 2
+LLAMA_VOCAB_TYPE_NONE = 0
+LLAMA_VOCAB_TYPE_SPM = 1
+LLAMA_VOCAB_TYPE_BPE = 2
+LLAMA_VOCAB_TYPE_WPM = 3
 
 
 # // note: these values should be synchronized with ggml_rope
@@ -548,8 +550,9 @@ class llama_model_params(ctypes.Structure):
 # struct llama_context_params {
 #     uint32_t seed;              // RNG seed, -1 for random
 #     uint32_t n_ctx;             // text context, 0 = from model
-#     uint32_t n_batch;           // prompt processing maximum batch size
-#     uint32_t n_parallel;        // number of parallel sequences (i.e. distinct states for recurrent models)
+#     uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
+#     uint32_t n_ubatch;          // physical maximum batch size
+#     uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
 #     uint32_t n_threads;         // number of threads to use for generation
 #     uint32_t n_threads_batch;   // number of threads to use for batch processing
 
@@ -590,8 +593,9 @@ class llama_context_params(ctypes.Structure):
     Attributes:
         seed (int): RNG seed, -1 for random
         n_ctx (int): text context, 0 = from model
-        n_batch (int): prompt processing maximum batch size
-        n_parallel (int): number of parallel sequences (i.e. distinct states for recurrent models)
+        n_batch (int): logical maximum batch size that can be submitted to llama_decode
+        n_ubatch (int): physical maximum batch size
+        n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models)
         n_threads (int): number of threads to use for generation
         n_threads_batch (int): number of threads to use for batch processing
         rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
@@ -619,7 +623,8 @@ class llama_context_params(ctypes.Structure):
         ("seed", ctypes.c_uint32),
         ("n_ctx", ctypes.c_uint32),
         ("n_batch", ctypes.c_uint32),
-        ("n_parallel", ctypes.c_uint32),
+        ("n_ubatch", ctypes.c_uint32),
+        ("n_seq_max", ctypes.c_uint32),
         ("n_threads", ctypes.c_uint32),
         ("n_threads_batch", ctypes.c_uint32),
         ("rope_scaling_type", ctypes.c_int),
@@ -667,7 +672,7 @@ It might not exist for progress report where '.' is output repeatedly."""
 #     bool allow_requantize;       // allow quantizing non-f32/f16 tensors
 #     bool quantize_output_tensor; // quantize output.weight
 #     bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-#     bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
+#     bool pure;                   // quantize all tensors to the default type
 #     void * imatrix;              // pointer to importance matrix data
 # } llama_model_quantize_params;
 class llama_model_quantize_params(ctypes.Structure):
@@ -679,7 +684,7 @@ class llama_model_quantize_params(ctypes.Structure):
         allow_requantize (bool): allow quantizing non-f32/f16 tensors
         quantize_output_tensor (bool): quantize output.weight
         only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-        pure (bool): disable k-quant mixtures and quantize all tensors to the same type
+        pure (bool): quantize all tensors to the default type
         imatrix (ctypes.ctypes.c_void_p): pointer to importance matrix data
     """
 
@@ -860,8 +865,7 @@ GGML_NUMA_STRATEGY_COUNT = 5
     [ctypes.c_int],
     None,
 )
-def llama_numa_init(numa: int, /):
-    ...
+def llama_numa_init(numa: int, /): ...
 
 
 # // Call once at the end of the program - currently only used for MPI
@@ -886,8 +890,7 @@ def llama_backend_free():
 )
 def llama_load_model_from_file(
     path_model: bytes, params: llama_model_params, /
-) -> Optional[llama_model_p]:
-    ...
+) -> Optional[llama_model_p]: ...
 
 
 # LLAMA_API void llama_free_model(struct llama_model * model);
@@ -896,8 +899,7 @@ def llama_load_model_from_file(
     [llama_model_p_ctypes],
     None,
 )
-def llama_free_model(model: llama_model_p, /):
-    ...
+def llama_free_model(model: llama_model_p, /): ...
 
 
 # LLAMA_API struct llama_context * llama_new_context_with_model(
@@ -910,8 +912,7 @@ def llama_free_model(model: llama_model_p, /):
 )
 def llama_new_context_with_model(
     model: llama_model_p, params: llama_context_params, /
-) -> Optional[llama_context_p]:
-    ...
+) -> Optional[llama_context_p]: ...
 
 
 # // Frees all allocated memory
@@ -932,80 +933,77 @@ def llama_free(ctx: llama_context_p, /):
     [],
     ctypes.c_int64,
 )
-def llama_time_us() -> int:
-    ...
+def llama_time_us() -> int: ...
 
 
 # LLAMA_API size_t llama_max_devices(void);
 @ctypes_function("llama_max_devices", [], ctypes.c_size_t)
-def llama_max_devices() -> int:
-    ...
+def llama_max_devices() -> int: ...
 
 
 # LLAMA_API bool llama_supports_mmap       (void);
 @ctypes_function("llama_supports_mmap", [], ctypes.c_bool)
-def llama_supports_mmap() -> bool:
-    ...
+def llama_supports_mmap() -> bool: ...
 
 
 # LLAMA_API bool llama_supports_mlock      (void);
 @ctypes_function("llama_supports_mlock", [], ctypes.c_bool)
-def llama_supports_mlock() -> bool:
-    ...
+def llama_supports_mlock() -> bool: ...
 
 
 # LLAMA_API bool llama_supports_gpu_offload(void);
 @ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool)
-def llama_supports_gpu_offload() -> bool:
-    ...
+def llama_supports_gpu_offload() -> bool: ...
 
 
 # LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
 @ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes)
-def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]:
-    ...
+def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ...
 
 
 # LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
 @ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32)
-def llama_n_ctx(ctx: llama_context_p, /) -> int:
-    ...
+def llama_n_ctx(ctx: llama_context_p, /) -> int: ...
 
 
 # LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
 @ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32)
-def llama_n_batch(ctx: llama_context_p, /) -> int:
-    ...
+def llama_n_batch(ctx: llama_context_p, /) -> int: ...
+
+
+# LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
+@ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32)
+def llama_n_ubatch(ctx: llama_context_p, /) -> int: ...
+
+
+# LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
+@ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32)
+def llama_n_seq_max(ctx: llama_context_p, /) -> int: ...
 
 
 # LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
 @ctypes_function("llama_vocab_type", [llama_model_p_ctypes], ctypes.c_int)
-def llama_vocab_type(model: llama_model_p, /) -> int:
-    ...
+def llama_vocab_type(model: llama_model_p, /) -> int: ...
 
 
 # LLAMA_API enum llama_rope_type  llama_rope_type (const struct llama_model * model);
 @ctypes_function("llama_rope_type", [llama_model_p_ctypes], ctypes.c_int)
-def llama_rope_type(model: llama_model_p, /) -> int:
-    ...
+def llama_rope_type(model: llama_model_p, /) -> int: ...
 
 
 # LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
 @ctypes_function("llama_n_vocab", [llama_model_p_ctypes], ctypes.c_int32)
-def llama_n_vocab(model: llama_model_p, /) -> int:
-    ...
+def llama_n_vocab(model: llama_model_p, /) -> int: ...
 
 
 # LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
 @ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
-def llama_n_ctx_train(model: llama_model_p, /) -> int:
-    ...
+def llama_n_ctx_train(model: llama_model_p, /) -> int: ...
 
 
 # LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
 @ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
-def llama_n_embd(model: llama_model_p, /) -> int:
-    ...
+def llama_n_embd(model: llama_model_p, /) -> int: ...
 
 
 # // Get the model's RoPE frequency scaling factor
@@ -1192,8 +1190,7 @@ def llama_model_apply_lora_from_file(
     path_base_model: Union[ctypes.c_char_p, bytes, None],
     n_threads: Union[ctypes.c_int32, int],
     /,
-) -> int:
-    ...
+) -> int: ...
 
 
 # //
@@ -1219,7 +1216,7 @@ class llama_kv_cache_view_cell(ctypes.Structure):
 #     // Maximum number of sequences that can exist in a cell. It's not an error
 #     // if there are more sequences in a cell than this value, however they will
 #     // not be visible in the view cells_sequences.
-#     int32_t n_max_seq;
+#     int32_t n_seq_max;
 
 #     // Number of tokens in the cache. For example, if there are two populated
 #     // cells, the first with 1 sequence id in it and the second with 2 sequence
@@ -1240,7 +1237,7 @@ class llama_kv_cache_view_cell(ctypes.Structure):
 #     struct llama_kv_cache_view_cell * cells;
 
 
-#     // The sequences for each cell. There will be n_max_seq items per cell.
+#     // The sequences for each cell. There will be n_seq_max items per cell.
 #     llama_seq_id * cells_sequences;
 # };
 class llama_kv_cache_view(ctypes.Structure):
@@ -1260,14 +1257,14 @@ llama_kv_cache_view_p = ctypes.POINTER(llama_kv_cache_view)
 
 
 # // Create an empty KV cache view. (use only for debugging purposes)
-# LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
+# LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
 @ctypes_function(
     "llama_kv_cache_view_init",
     [llama_context_p_ctypes, ctypes.c_int32],
     llama_kv_cache_view,
 )
 def llama_kv_cache_view_init(
-    ctx: llama_context_p, n_max_seq: Union[ctypes.c_int32, int], /
+    ctx: llama_context_p, n_seq_max: Union[ctypes.c_int32, int], /
 ) -> llama_kv_cache_view:
     """Create an empty KV cache view. (use only for debugging purposes)"""
     ...
@@ -1582,8 +1579,7 @@ def llama_load_session_file(
     n_token_capacity: Union[ctypes.c_size_t, int],
     n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
     /,
-) -> int:
-    ...
+) -> int: ...
 
 
 # LLAMA_API bool llama_save_session_file(
@@ -1607,8 +1603,7 @@ def llama_save_session_file(
     tokens: CtypesArray[llama_token],
     n_token_count: Union[ctypes.c_size_t, int],
     /,
-) -> int:
-    ...
+) -> int: ...
 
 
 # //
@@ -1728,6 +1723,17 @@ def llama_set_n_threads(
     """
     ...
 
+
+# // Set whether to use causal attention or not
+# // If set to true, the model will only attend to the past tokens
+# LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
+@ctypes_function("llama_set_causal_attn", [llama_context_p_ctypes, ctypes.c_bool], None)
+def llama_set_causal_attn(ctx: llama_context_p, causal_attn: bool, /):
+    """Set whether to use causal attention or not
+    If set to true, the model will only attend to the past tokens"""
+    ...
+
+
 # // Set abort callback
 # LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
 @ctypes_function(
@@ -1745,6 +1751,18 @@ def llama_set_abort_callback(
     ...
 
 
+# // Wait until all computations are finished
+# // This is automatically done when using one of the functions below to obtain the computation results
+# // and is not necessary to call it explicitly in most cases
+# LLAMA_API void llama_synchronize(struct llama_context * ctx);
+@ctypes_function("llama_synchronize", [llama_context_p_ctypes], None)
+def llama_synchronize(ctx: llama_context_p, /):
+    """Wait until all computations are finished
+    This is automatically done when using one of the functions below to obtain the computation results
+    and is not necessary to call it explicitly in most cases"""
+    ...
+
+
 # // Token logits obtained from the last call to llama_decode()
 # // The logits for the last token are stored in the last row
 # // Logits for which llama_batch.logits[i] == 0 are undefined
@@ -1760,7 +1778,7 @@ def llama_get_logits(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]:
     Logits for which llama_batch.logits[i] == 0 are undefined
     Rows: n_tokens provided with llama_batch
     Cols: n_vocab
-    
+
     Returns:
         Pointer to the logits buffer of shape (n_tokens, n_vocab)"""
     ...
@@ -1828,6 +1846,7 @@ def llama_get_embeddings_seq(
     shape: [n_embd] (1-dimensional)"""
     ...
 
+
 # //
 # // Vocab
 # //
@@ -1839,8 +1858,7 @@ def llama_get_embeddings_seq(
 )
 def llama_token_get_text(
     model: llama_model_p, token: Union[llama_token, int], /
-) -> bytes:
-    ...
+) -> bytes: ...
 
 
 # LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
@@ -1849,8 +1867,7 @@ def llama_token_get_text(
 )
 def llama_token_get_score(
     model: llama_model_p, token: Union[llama_token, int], /
-) -> float:
-    ...
+) -> float: ...
 
 
 # LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
@@ -1859,8 +1876,7 @@ def llama_token_get_score(
 )
 def llama_token_get_type(
     model: llama_model_p, token: Union[llama_token, int], /
-) -> int:
-    ...
+) -> int: ...
 
 
 # // Special tokens
@@ -1913,20 +1929,17 @@ def llama_token_prefix(model: llama_model_p) -> int:
 
 # LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
 @ctypes_function("llama_token_middle", [llama_model_p_ctypes], llama_token)
-def llama_token_middle(model: llama_model_p, /) -> int:
-    ...
+def llama_token_middle(model: llama_model_p, /) -> int: ...
 
 
 # LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
 @ctypes_function("llama_token_suffix", [llama_model_p_ctypes], llama_token)
-def llama_token_suffix(model: llama_model_p, /) -> int:
-    ...
+def llama_token_suffix(model: llama_model_p, /) -> int: ...
 
 
 # LLAMA_API llama_token llama_token_eot   (const struct llama_model * model); // End of infill middle
 @ctypes_function("llama_token_eot", [llama_model_p_ctypes], llama_token)
-def llama_token_eot(model: llama_model_p, /) -> int:
-    ...
+def llama_token_eot(model: llama_model_p, /) -> int: ...
 
 
 # //
@@ -1936,7 +1949,7 @@ def llama_token_eot(model: llama_model_p, /) -> int:
 
 # /// @details Convert the provided text into tokens.
 # /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
-# /// @return Returns the number of tokens on success, no more than n_max_tokens
+# /// @return Returns the number of tokens on success, no more than n_tokens_max
 # /// @return Returns a negative number on failure - the number of tokens that would have been returned
 # /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
 # ///                Does not insert a leading space.
@@ -1945,7 +1958,7 @@ def llama_token_eot(model: llama_model_p, /) -> int:
 #                   const char * text,
 #                      int32_t   text_len,
 #                  llama_token * tokens,
-#                      int32_t   n_max_tokens,
+#                      int32_t   n_tokens_max,
 #                         bool   add_bos,
 #                         bool   special);
 @ctypes_function(
@@ -1966,12 +1979,26 @@ def llama_tokenize(
     text: bytes,
     text_len: Union[ctypes.c_int, int],
     tokens: CtypesArray[llama_token],
-    n_max_tokens: Union[ctypes.c_int, int],
+    n_tokens_max: Union[ctypes.c_int, int],
     add_bos: Union[ctypes.c_bool, bool],
     special: Union[ctypes.c_bool, bool],
     /,
 ) -> int:
-    """Convert the provided text into tokens."""
+    """Convert the provided text into tokens.
+    
+    Args:
+        model: The model to use for tokenization.
+        text: The text to tokenize.
+        text_len: The length of the text.
+        tokens: The tokens pointer must be large enough to hold the resulting tokens.
+        n_max_tokens: The maximum number of tokens to return.
+        add_bos: Whether to add a beginning-of-sentence token.
+        special: Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
+                 Does not insert a leading space.
+                 
+    Returns:
+        Returns the number of tokens on success, no more than n_tokens_max
+        Returns a negative number on failure - the number of tokens that would have been returned"""
     ...
 
 
@@ -2043,8 +2070,7 @@ def llama_chat_apply_template(
     chat: CtypesArray[llama_chat_message],
     n_msg: int,
     /,
-) -> int:
-    ...
+) -> int: ...
 
 
 # //
@@ -2645,8 +2671,7 @@ def llama_beam_search(
     n_past: Union[ctypes.c_int, int],
     n_predict: Union[ctypes.c_int, int],
     /,
-):
-    ...
+): ...
 
 
 # Performance information
@@ -2723,5 +2748,4 @@ def llama_log_set(
     [ctypes.c_void_p, llama_context_p_ctypes],
     None,
 )
-def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p, /):
-    ...
+def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p, /): ...

vendor/llama.cpp

@@ -1 +1 @@
-Subproject commit c2101a2e909ac7c08976d414e64e96c90ee5fa9e
+Subproject commit 4e9a7f7f7fb6acbddd1462909c8d696e38edbfcc