1eaace8ea3
* Fix low_level_api_chat_cpp to match current API * Fix low_level_api_chat_cpp to match current API * Using None instead of empty string to so that default prompt template can be used if no prompt provided --------- Co-authored-by: Anil Pathak <anil@heyday.com>
202 lines
8.8 KiB
Python
202 lines
8.8 KiB
Python
import os
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import argparse
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import re
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from dataclasses import dataclass, field
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from typing import List
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# Based on https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
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@dataclass
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class GptParams:
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seed: int = -1
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n_threads: int = min(4, os.cpu_count() or 1)
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n_predict: int = 128
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n_parts: int = -1
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n_ctx: int = 512
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n_batch: int = 8
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n_keep: int = 0
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ignore_eos: bool = False
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logit_bias: dict[int, float] = field(default_factory=dict)
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top_k: int = 40
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top_p: float = 0.95
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tfs_z: float = 1.00
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typical_p: float = 1.00
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temp: float = 0.80
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repeat_penalty: float = 1.10
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repeat_last_n: int = 64
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frequency_penalty: float = 0.0
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presence_penalty: float = 0.0
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mirostat: int = 0
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mirostat_tau: float = 5.0
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mirostat_eta: float = 0.1
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model: str = "./models/llama-7B/ggml-model.bin"
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prompt: str = ""
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path_session: str = ""
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input_prefix: str = " "
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input_suffix: str = ""
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antiprompt: List[str] = field(default_factory=list)
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lora_adapter: str = ""
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lora_base: str = ""
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memory_f16: bool = True
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random_prompt: bool = False
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use_color: bool = False
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interactive: bool = False
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embedding: bool = False
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interactive_start: bool = False
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instruct: bool = False
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penalize_nl: bool = True
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perplexity: bool = False
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use_mmap: bool = True
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use_mlock: bool = False
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mem_test: bool = False
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verbose_prompt: bool = False
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file: str = None
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# If chat ended prematurely, append this to the conversation to fix it.
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# Set to "\nUser:" etc.
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# This is an alternative to input_prefix which always adds it, so it potentially duplicates "User:""
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fix_prefix: str = ""
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input_echo: bool = True,
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# Default instructions for Alpaca
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# switch to "Human" and "Assistant" for Vicuna.
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# TODO: TBD how they are gonna handle this upstream
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instruct_inp_prefix: str="\n\n### Instruction:\n\n"
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instruct_inp_suffix: str="\n\n### Response:\n\n"
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def gpt_params_parse(argv = None):
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parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument("-s", "--seed", type=int, default=-1, help="RNG seed (use random seed for <= 0)",dest="seed")
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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")
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parser.add_argument("-n", "--n_predict", type=int, default=128, help="number of tokens to predict (-1 = infinity)",dest="n_predict")
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parser.add_argument("--n_parts", type=int, default=-1, help="number of model parts", dest="n_parts")
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parser.add_argument("-c", "--ctx_size", type=int, default=512, help="size of the prompt context",dest="n_ctx")
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parser.add_argument("-b", "--batch_size", type=int, default=8, help="batch size for prompt processing",dest="n_batch")
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parser.add_argument("--keep", type=int, default=0, help="number of tokens to keep from the initial prompt",dest="n_keep")
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parser.add_argument(
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"-l",
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"--logit-bias",
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type=str,
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action='append',
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help="--logit-bias TOKEN_ID(+/-)BIAS",
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dest="logit_bias_str"
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)
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parser.add_argument("--ignore-eos", action="store_true", help="ignore end of stream token and continue generating", dest="ignore_eos")
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parser.add_argument("--top_k", type=int, default=40, help="top-k sampling",dest="top_k")
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parser.add_argument("--top_p", type=float, default=0.95, help="top-p samplin",dest="top_p")
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parser.add_argument("--tfs", type=float, default=1.0, help="tail free sampling, parameter z (1.0 = disabled)",dest="tfs_z")
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parser.add_argument("--temp", type=float, default=0.80, help="temperature",dest="temp")
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parser.add_argument("--repeat_penalty", type=float, default=1.10, help="penalize repeat sequence of tokens",dest="repeat_penalty")
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parser.add_argument("--repeat_last_n", type=int, default=64, help="last n tokens to consider for penalize ",dest="repeat_last_n")
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parser.add_argument("--frequency_penalty", type=float, default=0.0, help="repeat alpha frequency penalty (0.0 = disabled)",dest="tfs_z")
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parser.add_argument("--presence_penalty", type=float, default=0.0, help="repeat alpha presence penalty (0.0 = disabled)",dest="presence_penalty")
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parser.add_argument("--mirostat", type=float, default=1.0, help="use Mirostat sampling.",dest="mirostat")
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parser.add_argument("--mirostat_ent", type=float, default=5.0, help="Mirostat target entropy, parameter tau represents the average surprise value",dest="mirostat_tau")
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parser.add_argument("--mirostat_lr", type=float, default=0.1, help="Mirostat learning rate, parameter eta",dest="mirostat_eta")
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parser.add_argument("-m", "--model", type=str, default="./models/llama-7B/ggml-model.bin", help="model path",dest="model")
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parser.add_argument("-p", "--prompt", type=str, default=None, help="initial prompt",dest="prompt")
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parser.add_argument("-f", "--file", type=str, default=None, help="file containing initial prompt to load",dest="file")
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parser.add_argument("--session", type=str, default=None, help="file to cache model state in (may be large!)",dest="path_session")
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parser.add_argument("--in-prefix", type=str, default="", help="string to prefix user inputs with", dest="input_prefix")
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parser.add_argument("--in-suffix", type=str, default="", help="append to input", dest="input_suffix")
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parser.add_argument(
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"-r",
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"--reverse-prompt",
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type=str,
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action='append',
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help="poll user input upon seeing PROMPT (can be\nspecified more than once for multiple prompts).",
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dest="antiprompt"
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)
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parser.add_argument("--lora", type=str, default="", help="apply LoRA adapter (implies --no-mmap)", dest="lora_adapter")
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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")
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parser.add_argument("--memory_f32", action="store_false", help="use f32 instead of f16 for memory key+value",dest="memory_f16")
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parser.add_argument("--random-prompt", action="store_true", help="start with a randomized prompt.", dest="random_prompt")
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parser.add_argument(
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"--color",
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action="store_true",
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help="colorise output to distinguish prompt and user input from generations",
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dest="use_color"
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)
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parser.add_argument(
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"-i", "--interactive", action="store_true", help="run in interactive mode", dest="interactive"
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)
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parser.add_argument("--embedding", action="store_true", help="", dest="embedding")
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parser.add_argument(
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"--interactive-first",
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action="store_true",
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help="run in interactive mode and wait for input right away",
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dest="interactive_start"
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)
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parser.add_argument(
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"-ins",
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"--instruct",
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action="store_true",
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help="run in instruction mode (use with Alpaca or Vicuna models)",
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dest="instruct"
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)
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parser.add_argument("--no-penalize-nl", action="store_false", help="do not penalize newline token", dest="penalize_nl")
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parser.add_argument("--perplexity", action="store_true", help="compute perplexity over the prompt", dest="perplexity")
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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")
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parser.add_argument("--mlock", action="store_true",help="force system to keep model in RAM rather than swapping or compressing",dest="use_mlock")
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parser.add_argument("--mtest", action="store_true",help="compute maximum memory usage",dest="mem_test")
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parser.add_argument("--verbose-prompt", action="store_true",help="print prompt before generation",dest="verbose_prompt")
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#Custom args
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parser.add_argument("--fix-prefix", type=str, default="", help="append to input when generated n_predict tokens", dest="fix_prefix")
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parser.add_argument("--input-noecho", action="store_false", help="dont output the input", dest="input_echo")
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parser.add_argument(
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"--interactive-start",
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action="store_true",
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help="run in interactive mode",
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dest="interactive"
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)
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args = parser.parse_args(argv)
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logit_bias_str = args.logit_bias_str
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delattr(args, "logit_bias_str")
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params = GptParams(**vars(args))
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if (params.lora_adapter):
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params.use_mmap = False
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if (logit_bias_str != None):
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for i in logit_bias_str:
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if (m := re.match(r"(\d+)([-+]\d+)", i)):
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params.logit_bias[int(m.group(1))] = float(m.group(2))
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return params
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def gpt_random_prompt(rng):
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return [
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"So",
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"Once upon a time",
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"When",
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"The",
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"After",
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"If",
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"import",
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"He",
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"She",
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"They",
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][rng % 10]
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if __name__ == "__main__":
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print(gpt_params_parse())
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