llama.cpp/llama_cpp/llama_cpp.py
2023-03-23 05:33:06 -04:00

157 lines
5.7 KiB
Python

import ctypes
from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure
import pathlib
# Load the library
libfile = pathlib.Path(__file__).parent.parent / "libllama.so"
lib = ctypes.CDLL(str(libfile))
# C types
llama_token = c_int
llama_token_p = POINTER(llama_token)
class llama_token_data(Structure):
_fields_ = [
('id', llama_token), # token id
('p', c_float), # probability of the token
('plog', c_float), # log probability of the token
]
llama_token_data_p = POINTER(llama_token_data)
class llama_context_params(Structure):
_fields_ = [
('n_ctx', c_int), # text context
('n_parts', c_int), # -1 for default
('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
]
llama_context_params_p = POINTER(llama_context_params)
llama_context_p = c_void_p
# C functions
lib.llama_context_default_params.argtypes = []
lib.llama_context_default_params.restype = llama_context_params
lib.llama_init_from_file.argtypes = [c_char_p, llama_context_params]
lib.llama_init_from_file.restype = llama_context_p
lib.llama_free.argtypes = [llama_context_p]
lib.llama_free.restype = None
lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int]
lib.llama_model_quantize.restype = c_int
lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int]
lib.llama_eval.restype = c_int
lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int, c_bool]
lib.llama_tokenize.restype = c_int
lib.llama_n_vocab.argtypes = [llama_context_p]
lib.llama_n_vocab.restype = c_int
lib.llama_n_ctx.argtypes = [llama_context_p]
lib.llama_n_ctx.restype = c_int
lib.llama_get_logits.argtypes = [llama_context_p]
lib.llama_get_logits.restype = POINTER(c_float)
lib.llama_token_to_str.argtypes = [llama_context_p, llama_token]
lib.llama_token_to_str.restype = c_char_p
lib.llama_token_bos.argtypes = []
lib.llama_token_bos.restype = llama_token
lib.llama_token_eos.argtypes = []
lib.llama_token_eos.restype = llama_token
lib.llama_sample_top_p_top_k.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_double, c_double, c_double]
lib.llama_sample_top_p_top_k.restype = llama_token
lib.llama_print_timings.argtypes = [llama_context_p]
lib.llama_print_timings.restype = None
lib.llama_reset_timings.argtypes = [llama_context_p]
lib.llama_reset_timings.restype = None
lib.llama_print_system_info.argtypes = []
lib.llama_print_system_info.restype = c_char_p
# Python functions
def llama_context_default_params() -> llama_context_params:
params = lib.llama_context_default_params()
return params
def llama_init_from_file(path_model: bytes, params: llama_context_params) -> llama_context_p:
"""Various functions for loading a ggml llama model.
Allocate (almost) all memory needed for the model.
Return NULL on failure """
return lib.llama_init_from_file(path_model, params)
def llama_free(ctx: llama_context_p):
"""Free all allocated memory"""
lib.llama_free(ctx)
def llama_model_quantize(fname_inp: bytes, fname_out: bytes, itype: c_int, qk: c_int) -> c_int:
"""Returns 0 on success"""
return lib.llama_model_quantize(fname_inp, fname_out, itype, qk)
def llama_eval(ctx: llama_context_p, tokens: llama_token_p, n_tokens: c_int, n_past: c_int, n_threads: c_int) -> c_int:
"""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"""
return lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
def llama_tokenize(ctx: llama_context_p, text: bytes, tokens: llama_token_p, n_max_tokens: c_int, add_bos: c_bool) -> 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"""
return lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
def llama_n_vocab(ctx: llama_context_p) -> c_int:
return lib.llama_n_vocab(ctx)
def llama_n_ctx(ctx: llama_context_p) -> c_int:
return lib.llama_n_ctx(ctx)
def llama_get_logits(ctx: llama_context_p):
"""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"""
return lib.llama_get_logits(ctx)
def llama_token_to_str(ctx: llama_context_p, token: int) -> bytes:
"""Token Id -> String. Uses the vocabulary in the provided context"""
return lib.llama_token_to_str(ctx, token)
def llama_token_bos() -> llama_token:
return lib.llama_token_bos()
def llama_token_eos() -> llama_token:
return lib.llama_token_eos()
def llama_sample_top_p_top_k(ctx: llama_context_p, last_n_tokens_data: llama_token_p, last_n_tokens_size: c_int, top_k: c_int, top_p: c_double, temp: c_double, repeat_penalty: c_double) -> llama_token:
return lib.llama_sample_top_p_top_k(ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty)
def llama_print_timings(ctx: llama_context_p):
lib.llama_print_timings(ctx)
def llama_reset_timings(ctx: llama_context_p):
lib.llama_reset_timings(ctx)
def llama_print_system_info() -> bytes:
"""Print system informaiton"""
return lib.llama_print_system_info()