llama.cpp python bindings

llama.py

import os
import sys
import ctypes

from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure

# Load the library
if sys.platform == 'win32':
    lib = ctypes.cdll.LoadLibrary(os.path.join(os.path.dirname(__file__), 'llama.dll'))
else:
    lib = ctypes.cdll.LoadLibrary(os.path.join(os.path.dirname(__file__), 'libllama.so'))

# 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: str, 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.encode('utf-8'), params)

def llama_free(ctx: llama_context_p):
    """Free all allocated memory"""
    lib.llama_free(ctx)

def llama_model_quantize(fname_inp: str, fname_out: str, itype: c_int, qk: c_int) -> c_int:
    """Returns 0 on success"""
    return lib.llama_model_quantize(fname_inp.encode('utf-8'), fname_out.encode('utf-8'), 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: str, 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.encode('utf-8'), 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) -> str:
    """Token Id -> String. Uses the vocabulary in the provided context"""
    return lib.llama_token_to_str(ctx, token).decode('utf-8')

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() -> str:
    """Print system informaiton"""
    return lib.llama_print_system_info().decode('utf-8')

llama_example.py

import llama as llama

# Initialize llama context
params = llama.llama_context_default_params()

n = 512

params.n_ctx = n
params.n_parts = -1
params.seed = 1679473604
params.f16_kv = False
params.logits_all = False
params.vocab_only = False

# Set model path accordingly
ctx = llama.llama_init_from_file('models/ggml-model-q4.bin', params)

# Tokenize text
tokens = (llama.llama_token * n)()
n_tokens = llama.llama_tokenize(ctx, 'Q: What is the capital of France? A: ', tokens, n, True)
if n_tokens < 0:
    print('Error: llama_tokenize() returned {}'.format(n_tokens))
    exit(1)

text = "".join(llama.llama_token_to_str(ctx, t) for t in tokens[:n_tokens])
print(text)

# Evaluate tokens
for i in range(3):
    r = llama.llama_eval(ctx, tokens, n_tokens, 0, 12)
    if r != 0:
        print('Error: llama_eval() returned {}'.format(r))
        exit(1)
    token = llama.llama_sample_top_p_top_k(ctx, tokens, n_tokens , top_k=40, top_p=0.95, temp=0.8, repeat_penalty=1.1)
    print(token)
    tokens[n_tokens] = token
    n_tokens += 1
    text = "".join(llama.llama_token_to_str(ctx, t) for t in tokens[:n_tokens])
    print(text)

# # Print timings
llama.llama_print_timings(ctx)

# # Free context
llama.llama_free(ctx)