import sys import os import ctypes from ctypes import ( c_double, c_int, c_float, c_char_p, c_int32, c_uint32, c_void_p, c_bool, POINTER, _Pointer, # type: ignore Structure, Array, c_uint8, c_size_t, ) import pathlib from typing import List, Union # Load the library def _load_shared_library(lib_base_name: str): # Construct the paths to the possible shared library names _base_path = pathlib.Path(__file__).parent.resolve() # Searching for the library in the current directory under the name "libllama" (default name # for llamacpp) and "llama" (default name for this repo) _lib_paths: List[pathlib.Path] = [] # Determine the file extension based on the platform if sys.platform.startswith("linux"): _lib_paths += [ _base_path / f"lib{lib_base_name}.so", ] elif sys.platform == "darwin": _lib_paths += [ _base_path / f"lib{lib_base_name}.so", _base_path / f"lib{lib_base_name}.dylib", ] elif sys.platform == "win32": _lib_paths += [ _base_path / f"{lib_base_name}.dll", ] else: raise RuntimeError("Unsupported platform") if "LLAMA_CPP_LIB" in os.environ: lib_base_name = os.environ["LLAMA_CPP_LIB"] _lib = pathlib.Path(lib_base_name) _base_path = _lib.parent.resolve() _lib_paths = [_lib.resolve()] cdll_args = dict() # type: ignore # Add the library directory to the DLL search path on Windows (if needed) if sys.platform == "win32" and sys.version_info >= (3, 8): os.add_dll_directory(str(_base_path)) if "CUDA_PATH" in os.environ: os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "bin")) os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "lib")) cdll_args["winmode"] = 0 # Try to load the shared library, handling potential errors for _lib_path in _lib_paths: if _lib_path.exists(): try: return ctypes.CDLL(str(_lib_path), **cdll_args) except Exception as e: raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}") raise FileNotFoundError( f"Shared library with base name '{lib_base_name}' not found" ) # Specify the base name of the shared library to load _lib_base_name = "llama" # Load the library _lib = _load_shared_library(_lib_base_name) # Misc c_float_p = POINTER(c_float) c_uint8_p = POINTER(c_uint8) c_size_t_p = POINTER(c_size_t) # llama.h bindings GGML_USE_CUBLAS = hasattr(_lib, "ggml_init_cublas") GGML_CUDA_MAX_DEVICES = ctypes.c_int(16) LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else ctypes.c_int(1) # #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt' LLAMA_FILE_MAGIC_GGJT = ctypes.c_uint(0x67676A74) # #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' LLAMA_FILE_MAGIC_GGLA = ctypes.c_uint(0x67676C61) # #define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf' LLAMA_FILE_MAGIC_GGMF = ctypes.c_uint(0x67676D66) # #define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml' LLAMA_FILE_MAGIC_GGML = ctypes.c_uint(0x67676D6C) # #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' LLAMA_FILE_MAGIC_GGSN = ctypes.c_uint(0x6767736E) # #define LLAMA_FILE_VERSION 3 LLAMA_FILE_VERSION = c_int(3) LLAMA_FILE_MAGIC = LLAMA_FILE_MAGIC_GGJT LLAMA_FILE_MAGIC_UNVERSIONED = LLAMA_FILE_MAGIC_GGML LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN LLAMA_SESSION_VERSION = c_int(1) # #define LLAMA_DEFAULT_SEED 0xFFFFFFFF LLAMA_DEFAULT_SEED = c_int(0xFFFFFFFF) # struct llama_model; llama_model_p = c_void_p # struct llama_context; llama_context_p = c_void_p # typedef int llama_token; llama_token = c_int llama_token_p = POINTER(llama_token) # typedef struct llama_token_data { # llama_token id; // token id # float logit; // log-odds of the token # float p; // probability of the token # } llama_token_data; class llama_token_data(Structure): _fields_ = [ ("id", llama_token), ("logit", c_float), ("p", c_float), ] llama_token_data_p = POINTER(llama_token_data) # typedef struct llama_token_data_array { # llama_token_data * data; # size_t size; # bool sorted; # } llama_token_data_array; class llama_token_data_array(Structure): _fields_ = [ ("data", llama_token_data_p), ("size", c_size_t), ("sorted", c_bool), ] llama_token_data_array_p = POINTER(llama_token_data_array) # typedef void (*llama_progress_callback)(float progress, void *ctx); llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p) # struct llama_context_params { # uint32_t seed; // RNG seed, -1 for random # int32_t n_ctx; // text context # int32_t n_batch; // prompt processing batch size # int32_t n_gpu_layers; // number of layers to store in VRAM # int32_t main_gpu; // the GPU that is used for scratch and small tensors # float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs # // called with a progress value between 0 and 1, pass NULL to disable # llama_progress_callback progress_callback; # // context pointer passed to the progress callback # void * progress_callback_user_data; # // Keep the booleans together to avoid misalignment during copy-by-value. # bool low_vram; // if true, reduce VRAM usage at the cost of performance # bool f16_kv; // use fp16 for KV cache # bool logits_all; // the llama_eval() call computes all logits, not just the last one # bool vocab_only; // only load the vocabulary, no weights # bool use_mmap; // use mmap if possible # bool use_mlock; // force system to keep model in RAM # bool embedding; // embedding mode only # }; class llama_context_params(Structure): _fields_ = [ ("seed", c_uint32), ("n_ctx", c_int32), ("n_batch", c_int32), ("n_gpu_layers", c_int32), ("main_gpu", c_int32), ("tensor_split", c_float * LLAMA_MAX_DEVICES.value), ("progress_callback", llama_progress_callback), ("progress_callback_user_data", c_void_p), ("low_vram", c_bool), ("f16_kv", c_bool), ("logits_all", c_bool), ("vocab_only", c_bool), ("use_mmap", c_bool), ("use_mlock", c_bool), ("embedding", c_bool), ] llama_context_params_p = POINTER(llama_context_params) # enum llama_ftype { # LLAMA_FTYPE_ALL_F32 = 0, # LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors # LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors # LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors # LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 # // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed # // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed # LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors # LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors # LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors # LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors # LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors # }; LLAMA_FTYPE_ALL_F32 = c_int(0) LLAMA_FTYPE_MOSTLY_F16 = c_int(1) LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2) LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3) LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int(4) LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7) LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8) LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9) LLAMA_FTYPE_MOSTLY_Q2_K = c_int(10) LLAMA_FTYPE_MOSTLY_Q3_K_S = c_int(11) LLAMA_FTYPE_MOSTLY_Q3_K_M = c_int(12) LLAMA_FTYPE_MOSTLY_Q3_K_L = c_int(13) LLAMA_FTYPE_MOSTLY_Q4_K_S = c_int(14) LLAMA_FTYPE_MOSTLY_Q4_K_M = c_int(15) LLAMA_FTYPE_MOSTLY_Q5_K_S = c_int(16) LLAMA_FTYPE_MOSTLY_Q5_K_M = c_int(17) LLAMA_FTYPE_MOSTLY_Q6_K = c_int(18) # // model quantization parameters # typedef struct llama_model_quantize_params { # int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() # enum llama_ftype ftype; // quantize to this llama_ftype # bool allow_requantize; // allow quantizing non-f32/f16 tensors # bool quantize_output_tensor; // quantize output.weight # } llama_model_quantize_params; class llama_model_quantize_params(Structure): _fields_ = [ ("nthread", c_int), ("ftype", c_int), ("allow_requantize", c_bool), ("quantize_output_tensor", c_bool), ] # // performance timing information # struct llama_timings { # double t_start_ms; # double t_end_ms; # double t_load_ms; # double t_sample_ms; # double t_p_eval_ms; # double t_eval_ms; # int32_t n_sample; # int32_t n_p_eval; # int32_t n_eval; # }; class llama_timings(Structure): _fields_ = [ ("t_start_ms", c_double), ("t_end_ms", c_double), ("t_load_ms", c_double), ("t_sample_ms", c_double), ("t_p_eval_ms", c_double), ("t_eval_ms", c_double), ("n_sample", c_int32), ("n_p_eval", c_int32), ("n_eval", c_int32), ] # LLAMA_API struct llama_context_params llama_context_default_params(); def llama_context_default_params() -> llama_context_params: return _lib.llama_context_default_params() _lib.llama_context_default_params.argtypes = [] _lib.llama_context_default_params.restype = llama_context_params # LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(); def llama_model_quantize_default_params() -> llama_model_quantize_params: return _lib.llama_model_quantize_default_params() _lib.llama_model_quantize_default_params.argtypes = [] _lib.llama_model_quantize_default_params.restype = llama_model_quantize_params # LLAMA_API bool llama_mmap_supported(); def llama_mmap_supported() -> bool: return _lib.llama_mmap_supported() _lib.llama_mmap_supported.argtypes = [] _lib.llama_mmap_supported.restype = c_bool # LLAMA_API bool llama_mlock_supported(); def llama_mlock_supported() -> bool: return _lib.llama_mlock_supported() _lib.llama_mlock_supported.argtypes = [] _lib.llama_mlock_supported.restype = c_bool # // TODO: not great API - very likely to change # // Initialize the llama + ggml backend # // If numa is true, use NUMA optimizations # // Call once at the start of the program # LLAMA_API void llama_backend_init(bool numa); def llama_backend_init(numa: c_bool): return _lib.llama_backend_init(numa) _lib.llama_backend_init.argtypes = [c_bool] _lib.llama_backend_init.restype = None # // Call once at the end of the program - currently only used for MPI # LLAMA_API void llama_backend_free(); def llama_backend_free(): return _lib.llama_backend_free() _lib.llama_backend_free.argtypes = [] _lib.llama_backend_free.restype = None # LLAMA_API struct llama_model * llama_load_model_from_file( # const char * path_model, # struct llama_context_params params); def llama_load_model_from_file( path_model: bytes, params: llama_context_params ) -> llama_model_p: return _lib.llama_load_model_from_file(path_model, params) _lib.llama_load_model_from_file.argtypes = [c_char_p, llama_context_params] _lib.llama_load_model_from_file.restype = llama_model_p # LLAMA_API void llama_free_model(struct llama_model * model); def llama_free_model(model: llama_model_p): return _lib.llama_free_model(model) _lib.llama_free_model.argtypes = [llama_model_p] _lib.llama_free_model.restype = None # LLAMA_API struct llama_context * llama_new_context_with_model( # struct llama_model * model, # struct llama_context_params params); def llama_new_context_with_model( model: llama_model_p, params: llama_context_params ) -> llama_context_p: return _lib.llama_new_context_with_model(model, params) _lib.llama_new_context_with_model.argtypes = [llama_model_p, llama_context_params] _lib.llama_new_context_with_model.restype = llama_context_p # LLAMA_API int64_t llama_time_us(); def llama_time_us() -> int: return _lib.llama_time_us() _lib.llama_time_us.argtypes = [] _lib.llama_time_us.restype = ctypes.c_int64 # // Various functions for loading a ggml llama model. # // Allocate (almost) all memory needed for the model. # // Return NULL on failure # LLAMA_API struct llama_context * llama_init_from_file( # const char * path_model, # struct llama_context_params params); def llama_init_from_file( path_model: bytes, params: llama_context_params ) -> llama_context_p: return _lib.llama_init_from_file(path_model, params) _lib.llama_init_from_file.argtypes = [c_char_p, llama_context_params] _lib.llama_init_from_file.restype = llama_context_p # Frees all allocated memory # LLAMA_API void llama_free(struct llama_context * ctx); def llama_free(ctx: llama_context_p): return _lib.llama_free(ctx) _lib.llama_free.argtypes = [llama_context_p] _lib.llama_free.restype = None # // Returns 0 on success # LLAMA_API int llama_model_quantize( # const char * fname_inp, # const char * fname_out, # const llama_model_quantize_params * params); def llama_model_quantize( fname_inp: bytes, fname_out: bytes, params, # type: POINTER(llama_model_quantize_params) # type: ignore ) -> int: return _lib.llama_model_quantize(fname_inp, fname_out, params) _lib.llama_model_quantize.argtypes = [ c_char_p, c_char_p, POINTER(llama_model_quantize_params), ] _lib.llama_model_quantize.restype = c_int # Apply a LoRA adapter to a loaded model # path_base_model is the path to a higher quality model to use as a base for # the layers modified by the adapter. Can be NULL to use the current loaded model. # The model needs to be reloaded before applying a new adapter, otherwise the adapter # will be applied on top of the previous one # Returns 0 on success # LLAMA_API int llama_apply_lora_from_file( # struct llama_context * ctx, # const char * path_lora, # const char * path_base_model, # int n_threads); def llama_apply_lora_from_file( ctx: llama_context_p, path_lora: c_char_p, path_base_model: c_char_p, n_threads: c_int, ) -> int: return _lib.llama_apply_lora_from_file(ctx, path_lora, path_base_model, n_threads) _lib.llama_apply_lora_from_file.argtypes = [llama_context_p, c_char_p, c_char_p, c_int] _lib.llama_apply_lora_from_file.restype = c_int # LLAMA_API int llama_model_apply_lora_from_file( # const struct llama_model * model, # const char * path_lora, # const char * path_base_model, # int n_threads); def llama_model_apply_lora_from_file( model: llama_model_p, path_lora: Union[c_char_p, bytes], path_base_model: Union[c_char_p, bytes], n_threads: c_int, ) -> int: return _lib.llama_model_apply_lora_from_file( model, path_lora, path_base_model, n_threads ) _lib.llama_model_apply_lora_from_file.argtypes = [ llama_model_p, c_char_p, c_char_p, c_int, ] _lib.llama_model_apply_lora_from_file.restype = c_int # Returns the number of tokens in the KV cache # LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx); def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int: return _lib.llama_get_kv_cache_token_count(ctx) _lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p] _lib.llama_get_kv_cache_token_count.restype = c_int # Sets the current rng seed. # LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed); def llama_set_rng_seed(ctx: llama_context_p, seed: c_uint32): return _lib.llama_set_rng_seed(ctx, seed) _lib.llama_set_rng_seed.argtypes = [llama_context_p, c_int] _lib.llama_set_rng_seed.restype = None # Returns the maximum size in bytes of the state (rng, logits, embedding # and kv_cache) - will often be smaller after compacting tokens # LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx); def llama_get_state_size(ctx: llama_context_p) -> int: return _lib.llama_get_state_size(ctx) _lib.llama_get_state_size.argtypes = [llama_context_p] _lib.llama_get_state_size.restype = c_size_t # Copies the state to the specified destination address. # Destination needs to have allocated enough memory. # Returns the number of bytes copied # LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst); def llama_copy_state_data( ctx: llama_context_p, dst # type: Array[c_uint8] ) -> int: return _lib.llama_copy_state_data(ctx, dst) _lib.llama_copy_state_data.argtypes = [llama_context_p, c_uint8_p] _lib.llama_copy_state_data.restype = c_size_t # Set the state reading from the specified address # Returns the number of bytes read # LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src); def llama_set_state_data( ctx: llama_context_p, src # type: Array[c_uint8] ) -> int: return _lib.llama_set_state_data(ctx, src) _lib.llama_set_state_data.argtypes = [llama_context_p, c_uint8_p] _lib.llama_set_state_data.restype = c_size_t # Save/load session file # LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out); def llama_load_session_file( ctx: llama_context_p, path_session: bytes, tokens_out, # type: Array[llama_token] n_token_capacity: c_size_t, n_token_count_out, # type: _Pointer[c_size_t] ) -> int: return _lib.llama_load_session_file( ctx, path_session, tokens_out, n_token_capacity, n_token_count_out ) _lib.llama_load_session_file.argtypes = [ llama_context_p, c_char_p, llama_token_p, c_size_t, c_size_t_p, ] _lib.llama_load_session_file.restype = c_size_t # LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count); def llama_save_session_file( ctx: llama_context_p, path_session: bytes, tokens, # type: Array[llama_token] n_token_count: c_size_t, ) -> int: return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count) _lib.llama_save_session_file.argtypes = [ llama_context_p, c_char_p, llama_token_p, c_size_t, ] _lib.llama_save_session_file.restype = c_size_t # Run the llama inference to obtain the logits and probabilities for the next token. # tokens + n_tokens is the provided batch of new tokens to process # n_past is the number of tokens to use from previous eval calls # Returns 0 on success # LLAMA_API int llama_eval( # struct llama_context * ctx, # const llama_token * tokens, # int n_tokens, # int n_past, # int n_threads); def llama_eval( ctx: llama_context_p, tokens, # type: Array[llama_token] n_tokens: c_int, n_past: c_int, n_threads: c_int, ) -> int: return _lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads) _lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int] _lib.llama_eval.restype = c_int # // Same as llama_eval, but use float matrix input directly. # LLAMA_API int llama_eval_embd( # struct llama_context * ctx, # const float * embd, # int n_tokens, # int n_past, # int n_threads); def llama_eval_embd( ctx: llama_context_p, embd, # type: Array[c_float] n_tokens: c_int, n_past: c_int, n_threads: c_int, ) -> int: return _lib.llama_eval_embd(ctx, embd, n_tokens, n_past, n_threads) _lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int, c_int, c_int] _lib.llama_eval_embd.restype = c_int # Convert the provided text into tokens. # The tokens pointer must be large enough to hold the resulting tokens. # Returns the number of tokens on success, no more than n_max_tokens # Returns a negative number on failure - the number of tokens that would have been returned # TODO: not sure if correct # LLAMA_API int llama_tokenize( # struct llama_context * ctx, # const char * text, # llama_token * tokens, # int n_max_tokens, # bool add_bos); def llama_tokenize( ctx: llama_context_p, text: bytes, tokens, # type: Array[llama_token] n_max_tokens: c_int, add_bos: c_bool, ) -> int: return _lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos) _lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int, c_bool] _lib.llama_tokenize.restype = c_int # LLAMA_API int llama_tokenize_with_model( # const struct llama_model * model, # const char * text, # llama_token * tokens, # int n_max_tokens, # bool add_bos); def llama_tokenize_with_model( model: llama_model_p, text: bytes, tokens, # type: Array[llama_token] n_max_tokens: c_int, add_bos: c_bool, ) -> int: return _lib.llama_tokenize_with_model(model, text, tokens, n_max_tokens, add_bos) # LLAMA_API int llama_n_vocab(const struct llama_context * ctx); def llama_n_vocab(ctx: llama_context_p) -> int: return _lib.llama_n_vocab(ctx) _lib.llama_n_vocab.argtypes = [llama_context_p] _lib.llama_n_vocab.restype = c_int # LLAMA_API int llama_n_ctx (const struct llama_context * ctx); def llama_n_ctx(ctx: llama_context_p) -> int: return _lib.llama_n_ctx(ctx) _lib.llama_n_ctx.argtypes = [llama_context_p] _lib.llama_n_ctx.restype = c_int # LLAMA_API int llama_n_embd (const struct llama_context * ctx); def llama_n_embd(ctx: llama_context_p) -> int: return _lib.llama_n_embd(ctx) _lib.llama_n_embd.argtypes = [llama_context_p] _lib.llama_n_embd.restype = c_int # LLAMA_API int llama_n_vocab_from_model(const struct llama_model * model); def llama_n_vocab_from_model(model: llama_model_p) -> int: return _lib.llama_n_vocab_from_model(model) _lib.llama_n_vocab_from_model.argtypes = [llama_model_p] _lib.llama_n_vocab_from_model.restype = c_int # LLAMA_API int llama_n_ctx_from_model (const struct llama_model * model); def llama_n_ctx_from_model(model: llama_model_p) -> int: return _lib.llama_n_ctx_from_model(model) _lib.llama_n_ctx_from_model.argtypes = [llama_model_p] _lib.llama_n_ctx_from_model.restype = c_int # LLAMA_API int llama_n_embd_from_model (const struct llama_model * model); def llama_n_embd_from_model(model: llama_model_p) -> int: return _lib.llama_n_embd_from_model(model) _lib.llama_n_embd_from_model.argtypes = [llama_model_p] _lib.llama_n_embd_from_model.restype = c_int # // Get the vocabulary as output parameters. # // Returns number of results. # LLAMA_API int llama_get_vocab( # const struct llama_context * ctx, # const char * * strings, # float * scores, # int capacity); def llama_get_vocab( ctx: llama_context_p, strings, # type: Array[c_char_p] # type: ignore scores, # type: Array[c_float] # type: ignore capacity: c_int, ) -> int: return _lib.llama_get_vocab(ctx, strings, scores, capacity) _lib.llama_get_vocab.argtypes = [llama_context_p, c_char_p, c_float, c_int] _lib.llama_get_vocab.restype = c_int # LLAMA_API int llama_get_vocab_from_model( # const struct llama_model * model, # const char * * strings, # float * scores, # int capacity); def llama_get_vocab_from_model( model: llama_model_p, strings, # type: Array[c_char_p] # type: ignore scores, # type: Array[c_float] # type: ignore capacity: c_int, ) -> int: return _lib.llama_get_vocab_from_model(model, strings, scores, capacity) # Token logits obtained from the last call to llama_eval() # The logits for the last token are stored in the last row # Can be mutated in order to change the probabilities of the next token # Rows: n_tokens # Cols: n_vocab # LLAMA_API float * llama_get_logits(struct llama_context * ctx); def llama_get_logits( ctx: llama_context_p, ): # type: (...) -> Array[float] # type: ignore return _lib.llama_get_logits(ctx) _lib.llama_get_logits.argtypes = [llama_context_p] _lib.llama_get_logits.restype = c_float_p # Get the embeddings for the input # shape: [n_embd] (1-dimensional) # LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); def llama_get_embeddings( ctx: llama_context_p, ): # type: (...) -> Array[float] # type: ignore return _lib.llama_get_embeddings(ctx) _lib.llama_get_embeddings.argtypes = [llama_context_p] _lib.llama_get_embeddings.restype = c_float_p # // Token Id -> String. Uses the vocabulary in the provided context # LLAMA_API const char * llama_token_to_str( # const struct llama_context * ctx, # llama_token token); def llama_token_to_str(ctx: llama_context_p, token: llama_token) -> bytes: return _lib.llama_token_to_str(ctx, token) _lib.llama_token_to_str.argtypes = [llama_context_p, llama_token] _lib.llama_token_to_str.restype = c_char_p # LLAMA_API const char * llama_token_to_str_with_model( # const struct llama_model * model, # llama_token token); def llama_token_to_str_with_model(model: llama_model_p, token: llama_token) -> bytes: return _lib.llama_token_to_str_with_model(model, token) _lib.llama_token_to_str_with_model.argtypes = [llama_model_p, llama_token] _lib.llama_token_to_str_with_model.restype = c_char_p # Special tokens # LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence def llama_token_bos() -> int: return _lib.llama_token_bos() _lib.llama_token_bos.argtypes = [] _lib.llama_token_bos.restype = llama_token # LLAMA_API llama_token llama_token_eos(); // end-of-sentence def llama_token_eos() -> int: return _lib.llama_token_eos() _lib.llama_token_eos.argtypes = [] _lib.llama_token_eos.restype = llama_token # LLAMA_API llama_token llama_token_nl(); // next-line def llama_token_nl() -> int: return _lib.llama_token_nl() _lib.llama_token_nl.argtypes = [] _lib.llama_token_nl.restype = llama_token # Sampling functions # @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix. # LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty); def llama_sample_repetition_penalty( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] last_tokens_data, # type: Array[llama_token] last_tokens_size: c_int, penalty: c_float, ): return _lib.llama_sample_repetition_penalty( ctx, candidates, last_tokens_data, last_tokens_size, penalty ) _lib.llama_sample_repetition_penalty.argtypes = [ llama_context_p, llama_token_data_array_p, llama_token_p, c_int, c_float, ] _lib.llama_sample_repetition_penalty.restype = None # @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details. # LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence); def llama_sample_frequency_and_presence_penalties( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] last_tokens_data, # type: Array[llama_token] last_tokens_size: c_int, alpha_frequency: c_float, alpha_presence: c_float, ): return _lib.llama_sample_frequency_and_presence_penalties( ctx, candidates, last_tokens_data, last_tokens_size, alpha_frequency, alpha_presence, ) _lib.llama_sample_frequency_and_presence_penalties.argtypes = [ llama_context_p, llama_token_data_array_p, llama_token_p, c_int, c_float, c_float, ] _lib.llama_sample_frequency_and_presence_penalties.restype = None # /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806 # /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted. # /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. # /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. # /// @params smooth_factor Smooth factor between guidance logits and original logits. 1.0f means only use guidance logits. 0.0f means only original logits. # LLAMA_API void llama_sample_classifier_free_guidance( # struct llama_context * ctx, # llama_token_data_array * candidates, # struct llama_context * guidance_ctx, # float scale, # float smooth_factor); def llama_sample_classifier_free_guidance( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] guidance_ctx: llama_context_p, scale: c_float, smooth_factor: c_float, ): return _lib.llama_sample_classifier_free_guidance( ctx, candidates, guidance_ctx, scale, smooth_factor ) _lib.llama_sample_classifier_free_guidance.argtypes = [ llama_context_p, llama_token_data_array_p, llama_context_p, c_float, c_float, ] _lib.llama_sample_classifier_free_guidance.restype = None # @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. # LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates); def llama_sample_softmax( ctx: llama_context_p, candidates # type: _Pointer[llama_token_data] ): return _lib.llama_sample_softmax(ctx, candidates) _lib.llama_sample_softmax.argtypes = [ llama_context_p, llama_token_data_array_p, ] _lib.llama_sample_softmax.restype = None # @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 # LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep); def llama_sample_top_k( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] k: c_int, min_keep: c_size_t, ): return _lib.llama_sample_top_k(ctx, candidates, k, min_keep) _lib.llama_sample_top_k.argtypes = [ llama_context_p, llama_token_data_array_p, c_int, c_size_t, ] _lib.llama_sample_top_k.restype = None # @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 # LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep); def llama_sample_top_p( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] p: c_float, min_keep: c_size_t, ): return _lib.llama_sample_top_p(ctx, candidates, p, min_keep) _lib.llama_sample_top_p.argtypes = [ llama_context_p, llama_token_data_array_p, c_float, c_size_t, ] _lib.llama_sample_top_p.restype = None # @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/. # LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep); def llama_sample_tail_free( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] z: c_float, min_keep: c_size_t, ): return _lib.llama_sample_tail_free(ctx, candidates, z, min_keep) _lib.llama_sample_tail_free.argtypes = [ llama_context_p, llama_token_data_array_p, c_float, c_size_t, ] _lib.llama_sample_tail_free.restype = None # @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. # LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep); def llama_sample_typical( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] p: c_float, min_keep: c_size_t, ): return _lib.llama_sample_typical(ctx, candidates, p, min_keep) _lib.llama_sample_typical.argtypes = [ llama_context_p, llama_token_data_array_p, c_float, c_size_t, ] _lib.llama_sample_typical.restype = None # LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp); def llama_sample_temperature( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] temp: c_float, ): return _lib.llama_sample_temperature(ctx, candidates, temp) _lib.llama_sample_temperature.argtypes = [ llama_context_p, llama_token_data_array_p, c_float, ] _lib.llama_sample_temperature.restype = None # @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. # @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. # @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. # @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. # @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm. # @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. # LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu); def llama_sample_token_mirostat( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] tau: c_float, eta: c_float, m: c_int, mu, # type: _Pointer[c_float] ) -> int: return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu) _lib.llama_sample_token_mirostat.argtypes = [ llama_context_p, llama_token_data_array_p, c_float, c_float, c_int, c_float_p, ] _lib.llama_sample_token_mirostat.restype = llama_token # @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. # @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. # @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. # @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. # @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. # LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu); def llama_sample_token_mirostat_v2( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] tau: c_float, eta: c_float, mu, # type: _Pointer[c_float] ) -> int: return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu) _lib.llama_sample_token_mirostat_v2.argtypes = [ llama_context_p, llama_token_data_array_p, c_float, c_float, c_float_p, ] _lib.llama_sample_token_mirostat_v2.restype = llama_token # @details Selects the token with the highest probability. # LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates); def llama_sample_token_greedy( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] ) -> int: return _lib.llama_sample_token_greedy(ctx, candidates) _lib.llama_sample_token_greedy.argtypes = [ llama_context_p, llama_token_data_array_p, ] _lib.llama_sample_token_greedy.restype = llama_token # @details Randomly selects a token from the candidates based on their probabilities. # LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates); def llama_sample_token( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] ) -> int: return _lib.llama_sample_token(ctx, candidates) _lib.llama_sample_token.argtypes = [ llama_context_p, llama_token_data_array_p, ] _lib.llama_sample_token.restype = llama_token # Performance information # LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx); def llama_get_timings(ctx: llama_context_p) -> llama_timings: return _lib.llama_get_timings(ctx) _lib.llama_get_timings.argtypes = [llama_context_p] _lib.llama_get_timings.restype = llama_timings # LLAMA_API void llama_print_timings(struct llama_context * ctx); def llama_print_timings(ctx: llama_context_p): _lib.llama_print_timings(ctx) _lib.llama_print_timings.argtypes = [llama_context_p] _lib.llama_print_timings.restype = None # LLAMA_API void llama_reset_timings(struct llama_context * ctx); def llama_reset_timings(ctx: llama_context_p): _lib.llama_reset_timings(ctx) _lib.llama_reset_timings.argtypes = [llama_context_p] _lib.llama_reset_timings.restype = None # Print system information # LLAMA_API const char * llama_print_system_info(void); def llama_print_system_info() -> bytes: return _lib.llama_print_system_info() _lib.llama_print_system_info.argtypes = [] _lib.llama_print_system_info.restype = c_char_p ################################################################################################### _llama_initialized = False if not _llama_initialized: llama_backend_init(c_bool(False)) _llama_initialized = True