remove python bindings
This commit is contained in:
xaedes
parent
a5317498c2
commit
21b11b55d4
3 changed files with 0 additions and 342 deletions
|
|
@ -1,327 +0,0 @@
|
|||
import os
|
||||
import sys
|
||||
import glob
|
||||
import ctypes
|
||||
|
||||
from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, c_size_t, c_ubyte, POINTER, Structure
|
||||
|
||||
|
||||
# Load the library
|
||||
if sys.platform == 'win32':
|
||||
lib = ctypes.cdll.LoadLibrary(next(iter(glob.glob(os.path.join(os.path.dirname(__file__), '..', '..', '**', 'llama.dll'), recursive=True))))
|
||||
else:
|
||||
lib = ctypes.cdll.LoadLibrary(next(iter(glob.glob(os.path.join(os.path.dirname(__file__), '..', '..', '**', 'libllama.so'), recursive=True))))
|
||||
|
||||
|
||||
# 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_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
|
||||
('progress_callback', llama_progress_callback), # called with a progress value between 0 and 1, pass NULL to disable
|
||||
('progress_callback_user_data', c_void_p), # context pointer passed to the progress callback
|
||||
]
|
||||
|
||||
|
||||
llama_context_params_p = POINTER(llama_context_params)
|
||||
|
||||
llama_context_p = c_void_p
|
||||
|
||||
c_size_p = POINTER(c_size_t)
|
||||
c_ubyte_p = POINTER(c_ubyte)
|
||||
c_float_p = POINTER(c_float)
|
||||
|
||||
# C functions
|
||||
lib.llama_context_default_params.argtypes = []
|
||||
lib.llama_context_default_params.restype = llama_context_params
|
||||
|
||||
lib.llama_mmap_supported.argtypes = []
|
||||
lib.llama_mmap_supported.restype = c_bool
|
||||
|
||||
lib.llama_mlock_supported.argtypes = []
|
||||
lib.llama_mlock_supported.restype = c_bool
|
||||
|
||||
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_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
|
||||
|
||||
lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
|
||||
lib.llama_get_kv_cache_token_count.restype = c_int
|
||||
|
||||
lib.llama_set_rng_seed.argtypes = [llama_context_p, c_int]
|
||||
lib.llama_set_rng_seed.restype = None
|
||||
|
||||
lib.llama_get_state_size.argtypes = [llama_context_p]
|
||||
lib.llama_get_state_size.restype = c_size_t
|
||||
|
||||
lib.llama_copy_state_data.argtypes = [llama_context_p, c_ubyte_p]
|
||||
lib.llama_copy_state_data.restype = c_size_t
|
||||
|
||||
lib.llama_set_state_data.argtypes = [llama_context_p, c_ubyte_p]
|
||||
lib.llama_set_state_data.restype = c_size_t
|
||||
|
||||
lib.llama_load_session_file.argtypes = [llama_context_p, c_char_p, llama_token_p, c_size_t, c_size_p]
|
||||
lib.llama_load_session_file.restype = c_bool
|
||||
|
||||
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_bool
|
||||
|
||||
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_n_embd.argtypes = [llama_context_p]
|
||||
lib.llama_n_embd.restype = c_int
|
||||
|
||||
lib.llama_get_logits.argtypes = [llama_context_p]
|
||||
lib.llama_get_logits.restype = c_float_p
|
||||
|
||||
lib.llama_get_embeddings.argtypes = [llama_context_p]
|
||||
lib.llama_get_embeddings.restype = c_float_p
|
||||
|
||||
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_token_nl.argtypes = []
|
||||
lib.llama_token_nl.restype = llama_token
|
||||
|
||||
lib.llama_sample_repetition_penalty.argtypes = [llama_context_p, llama_token_data_array_p, llama_token_p, c_size_t, c_float]
|
||||
lib.llama_sample_repetition_penalty.restype = None
|
||||
|
||||
lib.llama_sample_frequency_and_presence_penalties.argtypes = [llama_context_p, llama_token_data_array_p, llama_token_p, c_size_t, c_float, c_float]
|
||||
lib.llama_sample_frequency_and_presence_penalties.restype = None
|
||||
|
||||
lib.llama_sample_softmax.argtypes = [llama_context_p, llama_token_data_array_p]
|
||||
lib.llama_sample_softmax.restype = None
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
lib.llama_sample_typical.argtypes = [llama_context_p, llama_token_data_array_p, c_float, c_size_t]
|
||||
lib.llama_sample_typical.restype = None
|
||||
|
||||
lib.llama_sample_temperature.argtypes = [llama_context_p, llama_token_data_array_p, c_float]
|
||||
lib.llama_sample_temperature.restype = None
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
lib.llama_sample_token_greedy.argtypes = [llama_context_p, llama_token_data_array_p]
|
||||
lib.llama_sample_token_greedy.restype = llama_token
|
||||
|
||||
lib.llama_sample_token.argtypes = [llama_context_p, llama_token_data_array_p]
|
||||
lib.llama_sample_token.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_mmap_supported() -> bool:
|
||||
return lib.llama_mmap_supported()
|
||||
|
||||
def llama_mlock_supported() -> bool:
|
||||
return lib.llama_mlock_supported()
|
||||
|
||||
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_apply_lora_from_file(ctx: llama_context_p, path_lora: str, path_base_model: str, n_threads: c_int) -> c_int:
|
||||
return lib.llama_apply_lora_from_file(ctx, path_lora.encode('utf-8'), path_base_model.encode('utf-8'), n_threads)
|
||||
|
||||
def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
|
||||
return lib.llama_get_kv_cache_token_count(ctx)
|
||||
|
||||
def llama_set_rng_seed(ctx: llama_context_p, seed: c_int):
|
||||
return lib.llama_set_rng_seed(ctx, seed)
|
||||
|
||||
def llama_get_state_size(ctx: llama_context_p) -> c_size_t:
|
||||
return lib.llama_get_state_size(ctx)
|
||||
|
||||
def llama_copy_state_data(ctx: llama_context_p, dst: c_ubyte_p) -> c_size_t:
|
||||
return lib.llama_copy_state_data(ctx, dst)
|
||||
|
||||
def llama_set_state_data(ctx: llama_context_p, src: c_ubyte_p) -> c_size_t:
|
||||
return lib.llama_set_state_data(ctx, src)
|
||||
|
||||
def llama_load_session_file(ctx: llama_context_p, path_session: str, tokens_out: llama_token_p, n_token_capacity: c_size_t, n_token_count_out: c_size_p) -> c_bool:
|
||||
return lib.llama_load_session_file(ctx, path_session.encode('utf-8'), tokens_out, n_token_capacity, n_token_count_out)
|
||||
|
||||
def llama_save_session_file(ctx: llama_context_p, path_session: str, tokens: llama_token_p, n_token_count: c_size_t) -> c_bool:
|
||||
return lib.llama_save_session_file(ctx, path_session.encode('utf-8'), tokens, n_token_count)
|
||||
|
||||
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_n_embd(ctx: llama_context_p) -> c_int:
|
||||
return lib.llama_n_embd(ctx)
|
||||
|
||||
def llama_get_logits(ctx: llama_context_p) -> c_float_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_get_embeddings(ctx: llama_context_p) -> c_float_p:
|
||||
"""Get the embeddings for the input
|
||||
shape: [n_embd] (1-dimensional)"""
|
||||
return lib.llama_get_embeddings(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', errors='ignore')
|
||||
|
||||
def llama_token_bos() -> llama_token:
|
||||
return lib.llama_token_bos()
|
||||
|
||||
def llama_token_eos() -> llama_token:
|
||||
return lib.llama_token_eos()
|
||||
|
||||
def llama_token_nl() -> llama_token:
|
||||
return lib.llama_token_nl()
|
||||
|
||||
def llama_sample_repetition_penalty(ctx: llama_context_p, candidates: llama_token_data_array_p, last_tokens: llama_token_p, last_tokens_size: c_size_t, penalty: float):
|
||||
lib.llama_sample_repetition_penalty(ctx, candidates, last_tokens, last_tokens_size, penalty)
|
||||
|
||||
def llama_sample_frequency_and_presence_penalties(ctx: llama_context_p, candidates: llama_token_data_array_p, last_tokens: llama_token_p, last_tokens_size: c_size_t, alpha_frequency: float, alpha_presence: float):
|
||||
lib.llama_sample_frequency_and_presence_penalties(ctx, candidates, last_tokens, last_tokens_size, alpha_frequency, alpha_presence)
|
||||
|
||||
def llama_sample_softmax(ctx: llama_context_p, candidates: llama_token_data_array_p):
|
||||
lib.llama_sample_softmax(ctx, candidates)
|
||||
|
||||
def llama_sample_top_k(ctx: llama_context_p, candidates: llama_token_data_array_p, k: c_int, min_keep: c_size_t):
|
||||
lib.llama_sample_top_k(ctx, candidates, k, min_keep)
|
||||
|
||||
def llama_sample_top_p(ctx: llama_context_p, candidates: llama_token_data_array_p, p: float, min_keep: c_size_t):
|
||||
lib.llama_sample_top_p(ctx, candidates, c_float(p), c_size_t(min_keep))
|
||||
|
||||
def llama_sample_tail_free(ctx: llama_context_p, candidates: llama_token_data_array_p, z: float, min_keep: c_size_t):
|
||||
lib.llama_sample_tail_free(ctx, candidates, z, min_keep)
|
||||
|
||||
def llama_sample_typical(ctx: llama_context_p, candidates: llama_token_data_array_p, p: float, min_keep: c_size_t):
|
||||
lib.llama_sample_typical(ctx, candidates, p, min_keep)
|
||||
|
||||
def llama_sample_temperature(ctx: llama_context_p, candidates: llama_token_data_array_p, temp: float):
|
||||
lib.llama_sample_temperature(ctx, candidates, temp)
|
||||
|
||||
def llama_sample_token_mirostat(ctx: llama_context_p, candidates: llama_token_data_array_p, tau: float, eta: float, m: c_int, mu: c_float_p) -> llama_token:
|
||||
return lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
|
||||
|
||||
def llama_sample_token_mirostat_v2(ctx: llama_context_p, candidates: llama_token_data_array_p, tau: float, eta: float, mu: c_float_p) -> llama_token:
|
||||
return lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
|
||||
|
||||
def llama_sample_token_greedy(ctx: llama_context_p, candidates: llama_token_data_array_p) -> llama_token:
|
||||
return lib.llama_sample_token_greedy(ctx, candidates)
|
||||
|
||||
def llama_sample_token(ctx: llama_context_p, candidates: llama_token_data_array_p) -> llama_token:
|
||||
return lib.llama_sample_token(ctx, candidates)
|
||||
|
||||
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() -> c_char_p:
|
||||
return lib.llama_print_system_info()
|
||||
15
setup.py
15
setup.py
|
|
@ -1,15 +0,0 @@
|
|||
|
||||
from setuptools import setup, find_packages
|
||||
import glob, os
|
||||
|
||||
setup(
|
||||
name='llama_cpp',
|
||||
version='0.0.1',
|
||||
author='Anonymous',
|
||||
author_email='',
|
||||
license='All rights reserved',
|
||||
packages=find_packages(where='py'),
|
||||
package_dir={'': 'py'},
|
||||
install_requires=[],
|
||||
entry_points={'console_scripts': []},
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue