alihan/llama-cpp-python
1
0
Fork 0
mirror of https://github.com/abetlen/llama-cpp-python.git synced 2023-09-07 17:34:22 +03:00
Code Issues Packages Projects Releases Wiki Activity

Black formatting

Browse Source
This commit is contained in:
Andrei Betlen
2023-03-24 14:35:41 -04:00
parent d29b05bb67
commit 2cc499512c
6 changed files with 121 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
llama_cpp/llama.py
View File

@@ -5,6 +5,7 @@ from typing import List, Optional
from . import llama_cpp
class Llama:
def __init__(
self,
@@ -82,7 +83,10 @@ class Llama:
for i in range(max_tokens):
tokens_seen = prompt_tokens + completion_tokens
last_n_tokens = [0] * max(0, self.last_n - tokens_seen) + [self.tokens[j] for j in range(max(tokens_seen - self.last_n, 0), tokens_seen)]
last_n_tokens = [0] * max(0, self.last_n - tokens_seen) + [
self.tokens[j]
for j in range(max(tokens_seen - self.last_n, 0), tokens_seen)
]
token = llama_cpp.llama_sample_top_p_top_k(
self.ctx,
@@ -128,9 +132,8 @@ class Llama:
self.ctx,
)[:logprobs]
return {
"id": f"cmpl-{str(uuid.uuid4())}", # Likely to change
"id": f"cmpl-{str(uuid.uuid4())}", # Likely to change
"object": "text_completion",
"created": int(time.time()),
"model": self.model_path,
@@ -151,5 +154,3 @@ class Llama:
def __del__(self):
llama_cpp.llama_free(self.ctx)

106
llama_cpp/llama_cpp.py
View File

@@ -1,6 +1,15 @@
import ctypes
from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure
from ctypes import (
c_int,
c_float,
c_double,
c_char_p,
c_void_p,
c_bool,
POINTER,
Structure,
)
import pathlib
@@ -13,26 +22,32 @@ lib = ctypes.CDLL(str(libfile))
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
("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
("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
@@ -74,7 +89,15 @@ 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.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]
@@ -86,45 +109,71 @@ 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:
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 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:
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:
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:
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"""
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
@@ -133,25 +182,42 @@ def llama_get_logits(ctx: llama_context_p):
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_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()