import sys import os import ctypes from ctypes import ( c_int, c_float, c_char_p, c_void_p, c_bool, POINTER, _Pointer, # type: ignore Structure, Array, c_uint8, c_size_t, ) import pathlib # Load the library def _load_shared_library(lib_base_name: str): # Determine the file extension based on the platform if sys.platform.startswith("linux"): lib_ext = ".so" elif sys.platform == "darwin": lib_ext = ".so" elif sys.platform == "win32": lib_ext = ".dll" else: raise RuntimeError("Unsupported platform") # 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 = [ _base_path / f"lib{lib_base_name}{lib_ext}", _base_path / f"{lib_base_name}{lib_ext}", ] 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) # C types LLAMA_FILE_VERSION = c_int(2) LLAMA_FILE_MAGIC = b"ggjt" LLAMA_FILE_MAGIC_UNVERSIONED = b"ggml" LLAMA_SESSION_MAGIC = b"ggsn" LLAMA_SESSION_VERSION = c_int(1) llama_context_p = c_void_p llama_token = c_int llama_token_p = POINTER(llama_token) class llama_token_data(Structure): _fields_ = [ ("id", llama_token), # token id ("logit", c_float), # log-odds of the token ("p", c_float), # probability of the token ] llama_token_data_p = POINTER(llama_token_data) 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) llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p) class llama_context_params(Structure): _fields_ = [ ("n_ctx", c_int), # text context ("n_parts", c_int), # -1 for default ("n_gpu_layers", c_int), # number of layers to store in VRAM ("seed", c_int), # RNG seed, 0 for random ("f16_kv", c_bool), # use fp16 for KV cache ( "logits_all", c_bool, ), # the llama_eval() call computes all logits, not just the last one ("vocab_only", c_bool), # only load the vocabulary, no weights ("use_mmap", c_bool), # use mmap if possible ("use_mlock", c_bool), # force system to keep model in RAM ("embedding", c_bool), # embedding mode only # called with a progress value between 0 and 1, pass NULL to disable ("progress_callback", llama_progress_callback), # context pointer passed to the progress callback ("progress_callback_user_data", c_void_p), ] llama_context_params_p = POINTER(llama_context_params) LLAMA_FTYPE_ALL_F32 = c_int(0) LLAMA_FTYPE_MOSTLY_F16 = c_int(1) # except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2) # except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3) # except 1d tensors LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int( 4 ) # tok_embeddings.weight and output.weight are F16 # LLAMA_FTYPE_MOSTLY_Q4_2 = c_int(5) # except 1d tensors # LLAMA_FTYPE_MOSTYL_Q4_3 = c_int(6) # except 1d tensors LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7) # except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8) # except 1d tensors LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9) # except 1d tensors # Misc c_float_p = POINTER(c_float) c_uint8_p = POINTER(c_uint8) c_size_t_p = POINTER(c_size_t) # Functions 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 def llama_mmap_supported() -> bool: return _lib.llama_mmap_supported() _lib.llama_mmap_supported.argtypes = [] _lib.llama_mmap_supported.restype = c_bool def llama_mlock_supported() -> bool: return _lib.llama_mlock_supported() _lib.llama_mlock_supported.argtypes = [] _lib.llama_mlock_supported.restype = c_bool # Various functions for loading a ggml llama model. # Allocate (almost) all memory needed for the model. # Return NULL on failure 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 def llama_free(ctx: llama_context_p): _lib.llama_free(ctx) _lib.llama_free.argtypes = [llama_context_p] _lib.llama_free.restype = None # TODO: not great API - very likely to change # Returns 0 on success # nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given def llama_model_quantize( fname_inp: bytes, fname_out: bytes, ftype: c_int, nthread: c_int ) -> c_int: return _lib.llama_model_quantize(fname_inp, fname_out, ftype, nthread) _lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int] _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 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, ) -> c_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 # Returns the number of tokens in the KV cache def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_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. def llama_set_rng_seed(ctx: llama_context_p, seed: c_int): 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 def llama_get_state_size(ctx: llama_context_p) -> c_size_t: 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 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 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 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] ) -> c_size_t: 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 def llama_save_session_file( ctx: llama_context_p, path_session: bytes, tokens, # type: Array[llama_token] n_token_count: c_size_t, ) -> c_size_t: 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 def llama_eval( ctx: llama_context_p, tokens, # type: Array[llama_token] n_tokens: c_int, n_past: c_int, n_threads: c_int, ) -> c_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 # 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 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 def llama_n_vocab(ctx: llama_context_p) -> c_int: return _lib.llama_n_vocab(ctx) _lib.llama_n_vocab.argtypes = [llama_context_p] _lib.llama_n_vocab.restype = c_int def llama_n_ctx(ctx: llama_context_p) -> c_int: return _lib.llama_n_ctx(ctx) _lib.llama_n_ctx.argtypes = [llama_context_p] _lib.llama_n_ctx.restype = c_int def llama_n_embd(ctx: llama_context_p) -> c_int: return _lib.llama_n_embd(ctx) _lib.llama_n_embd.argtypes = [llama_context_p] _lib.llama_n_embd.restype = c_int # 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 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) 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 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 # Special tokens def llama_token_bos() -> llama_token: return _lib.llama_token_bos() _lib.llama_token_bos.argtypes = [] _lib.llama_token_bos.restype = llama_token def llama_token_eos() -> llama_token: return _lib.llama_token_eos() _lib.llama_token_eos.argtypes = [] _lib.llama_token_eos.restype = llama_token def llama_token_nl() -> llama_token: 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. 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. 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 Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. 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 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 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/. 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. 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 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. 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] ) -> llama_token: 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. 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] ) -> llama_token: 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. def llama_sample_token_greedy( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] ) -> llama_token: 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. def llama_sample_token( ctx: llama_context_p, candidates, # type: _Pointer[llama_token_data_array] ) -> llama_token: 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 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 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 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