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import os
import argparse
import re
from dataclasses import dataclass, field
from typing import List
# Based on https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
@dataclass
class GptParams:
seed: int = -1
n_threads: int = min(4, os.cpu_count() or 1)
n_predict: int = 128
n_parts: int = -1
n_ctx: int = 512
n_batch: int = 8
n_keep: int = 0
ignore_eos: bool = False
logit_bias: dict[int, float] = field(default_factory=dict)
top_k: int = 40
top_p: float = 0.95
tfs_z: float = 1.00
typical_p: float = 1.00
temp: float = 0.80
repeat_penalty: float = 1.10
repeat_last_n: int = 64
frequency_penalty: float = 0.0
presence_penalty: float = 0.0
mirostat: int = 0
mirostat_tau: float = 5.0
mirostat_eta: float = 0.1
model: str = "./models/llama-7B/ggml-model.bin"
prompt: str = ""
path_session: str = ""
input_prefix: str = " "
input_suffix: str = ""
antiprompt: List[str] = field(default_factory=list)
lora_adapter: str = ""
lora_base: str = ""
memory_f16: bool = True
random_prompt: bool = False
use_color: bool = False
interactive: bool = False
embedding: bool = False
interactive_start: bool = False
instruct: bool = False
penalize_nl: bool = True
perplexity: bool = False
use_mmap: bool = True
use_mlock: bool = False
mem_test: bool = False
verbose_prompt: bool = False
file: str = None
# If chat ended prematurely, append this to the conversation to fix it.
# Set to "\nUser:" etc.
# This is an alternative to input_prefix which always adds it, so it potentially duplicates "User:""
fix_prefix: str = ""
input_echo: bool = (True,)
# Default instructions for Alpaca
# switch to "Human" and "Assistant" for Vicuna.
# TODO: TBD how they are gonna handle this upstream
instruct_inp_prefix: str = "\n\n### Instruction:\n\n"
instruct_inp_suffix: str = "\n\n### Response:\n\n"
def gpt_params_parse(argv=None):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=-1,
help="RNG seed (use random seed for <= 0)",
dest="seed",
)
parser.add_argument(
"-t",
"--threads",
type=int,
default=min(4, os.cpu_count() or 1),
help="number of threads to use during computation",
dest="n_threads",
)
parser.add_argument(
"-n",
"--n_predict",
type=int,
default=128,
help="number of tokens to predict (-1 = infinity)",
dest="n_predict",
)
parser.add_argument(
"--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts"
)
parser.add_argument(
"-c",
"--ctx_size",
type=int,
default=512,
help="size of the prompt context",
dest="n_ctx",
)
parser.add_argument(
"-b",
"--batch_size",
type=int,
default=8,
help="batch size for prompt processing",
dest="n_batch",
)
parser.add_argument(
"--keep",
type=int,
default=0,
help="number of tokens to keep from the initial prompt",
dest="n_keep",
)
parser.add_argument(
"-l",
"--logit-bias",
type=str,
action="append",
help="--logit-bias TOKEN_ID(+/-)BIAS",
dest="logit_bias_str",
)
parser.add_argument(
"--ignore-eos",
action="store_true",
help="ignore end of stream token and continue generating",
dest="ignore_eos",
)
parser.add_argument(
"--top_k", type=int, default=40, help="top-k sampling", dest="top_k"
)
parser.add_argument(
"--top_p", type=float, default=0.95, help="top-p samplin", dest="top_p"
)
parser.add_argument(
"--tfs",
type=float,
default=1.0,
help="tail free sampling, parameter z (1.0 = disabled)",
dest="tfs_z",
)
parser.add_argument(
"--temp", type=float, default=0.80, help="temperature", dest="temp"
)
parser.add_argument(
"--repeat_penalty",
type=float,
default=1.10,
help="penalize repeat sequence of tokens",
dest="repeat_penalty",
)
parser.add_argument(
"--repeat_last_n",
type=int,
default=64,
help="last n tokens to consider for penalize ",
dest="repeat_last_n",
)
parser.add_argument(
"--frequency_penalty",
type=float,
default=0.0,
help="repeat alpha frequency penalty (0.0 = disabled)",
dest="tfs_z",
)
parser.add_argument(
"--presence_penalty",
type=float,
default=0.0,
help="repeat alpha presence penalty (0.0 = disabled)",
dest="presence_penalty",
)
parser.add_argument(
"--mirostat",
type=float,
default=1.0,
help="use Mirostat sampling.",
dest="mirostat",
)
parser.add_argument(
"--mirostat_ent",
type=float,
default=5.0,
help="Mirostat target entropy, parameter tau represents the average surprise value",
dest="mirostat_tau",
)
parser.add_argument(
"--mirostat_lr",
type=float,
default=0.1,
help="Mirostat learning rate, parameter eta",
dest="mirostat_eta",
)
parser.add_argument(
"-m",
"--model",
type=str,
default="./models/llama-7B/ggml-model.bin",
help="model path",
dest="model",
)
parser.add_argument(
"-p", "--prompt", type=str, default=None, help="initial prompt", dest="prompt"
)
parser.add_argument(
"-f",
"--file",
type=str,
default=None,
help="file containing initial prompt to load",
dest="file",
)
parser.add_argument(
"--session",
type=str,
default=None,
help="file to cache model state in (may be large!)",
dest="path_session",
)
parser.add_argument(
"--in-prefix",
type=str,
default="",
help="string to prefix user inputs with",
dest="input_prefix",
)
parser.add_argument(
"--in-suffix", type=str, default="", help="append to input", dest="input_suffix"
)
parser.add_argument(
"-r",
"--reverse-prompt",
type=str,
action="append",
help="poll user input upon seeing PROMPT (can be\nspecified more than once for multiple prompts).",
dest="antiprompt",
)
parser.add_argument(
"--lora",
type=str,
default="",
help="apply LoRA adapter (implies --no-mmap)",
dest="lora_adapter",
)
parser.add_argument(
"--lora-base",
type=str,
default="",
help="optional model to use as a base for the layers modified by the LoRA adapter",
dest="lora_base",
)
parser.add_argument(
"--memory_f32",
action="store_false",
help="use f32 instead of f16 for memory key+value",
dest="memory_f16",
)
parser.add_argument(
"--random-prompt",
action="store_true",
help="start with a randomized prompt.",
dest="random_prompt",
)
parser.add_argument(
"--color",
action="store_true",
help="colorise output to distinguish prompt and user input from generations",
dest="use_color",
)
parser.add_argument(
"-i",
"--interactive",
action="store_true",
help="run in interactive mode",
dest="interactive",
)
parser.add_argument("--embedding", action="store_true", help="", dest="embedding")
parser.add_argument(
"--interactive-first",
action="store_true",
help="run in interactive mode and wait for input right away",
dest="interactive_start",
)
parser.add_argument(
"-ins",
"--instruct",
action="store_true",
help="run in instruction mode (use with Alpaca or Vicuna models)",
dest="instruct",
)
parser.add_argument(
"--no-penalize-nl",
action="store_false",
help="do not penalize newline token",
dest="penalize_nl",
)
parser.add_argument(
"--perplexity",
action="store_true",
help="compute perplexity over the prompt",
dest="perplexity",
)
parser.add_argument(
"--no-mmap",
action="store_false",
help="do not memory-map model (slower load but may reduce pageouts if not using mlock)",
dest="use_mmap",
)
parser.add_argument(
"--mlock",
action="store_true",
help="force system to keep model in RAM rather than swapping or compressing",
dest="use_mlock",
)
parser.add_argument(
"--mtest",
action="store_true",
help="compute maximum memory usage",
dest="mem_test",
)
parser.add_argument(
"--verbose-prompt",
action="store_true",
help="print prompt before generation",
dest="verbose_prompt",
)
# Custom args
parser.add_argument(
"--fix-prefix",
type=str,
default="",
help="append to input when generated n_predict tokens",
dest="fix_prefix",
)
parser.add_argument(
"--input-noecho",
action="store_false",
help="dont output the input",
dest="input_echo",
)
parser.add_argument(
"--interactive-start",
action="store_true",
help="run in interactive mode",
dest="interactive",
)
args = parser.parse_args(argv)
logit_bias_str = args.logit_bias_str
delattr(args, "logit_bias_str")
params = GptParams(**vars(args))
if params.lora_adapter:
params.use_mmap = False
if logit_bias_str != None:
for i in logit_bias_str:
if m := re.match(r"(\d+)([-+]\d+)", i):
params.logit_bias[int(m.group(1))] = float(m.group(2))
return params
def gpt_random_prompt(rng):
return [
"So",
"Once upon a time",
"When",
"The",
"After",
"If",
"import",
"He",
"She",
"They",
][rng % 10]
if __name__ == "__main__":
print(gpt_params_parse())
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