Better llama.cpp interoperability
Has some too many newline issues so WIP
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Mug
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0
examples/__init__.py
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0
examples/__init__.py
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135
examples/common.py
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examples/common.py
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@ -0,0 +1,135 @@
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import os
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import argparse
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from dataclasses import dataclass, field
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from typing import List, Optional
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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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repeat_last_n: int = 64
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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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top_k: int = 40
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top_p: float = 0.95
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temp: float = 0.80
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repeat_penalty: float = 1.10
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model: str = "./models/llama-7B/ggml-model.bin"
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prompt: str = ""
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input_prefix: str = " "
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fix_prefix: str = ""
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output_postfix: str = ""
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input_echo: bool = True,
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antiprompt: List[str] = field(default_factory=list)
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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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ignore_eos: bool = False
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perplexity: bool = False
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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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# Default instructions for Alpaca
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# switch to "Human" and "Assistant" for Vicuna.
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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, params: Optional[GptParams] = None):
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if params is None:
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params = GptParams()
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parser = argparse.ArgumentParser()
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parser.add_argument("-h", "--help", action="store_true", help="show this help message and exit")
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parser.add_argument("-s", "--seed", type=int, default=-1, help="",dest="seed")
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parser.add_argument("-t", "--threads", type=int, default=1, help="",dest="n_threads")
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parser.add_argument("-p", "--prompt", type=str, default="", help="",dest="prompt")
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parser.add_argument("-f", "--file", type=str, default=None, help="")
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parser.add_argument("-c", "--ctx_size", type=int, default=512, help="",dest="n_ctx")
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parser.add_argument("--memory_f32", action="store_false", help="",dest="memory_f16")
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parser.add_argument("--top_p", type=float, default=0.9, help="",dest="top_p")
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parser.add_argument("--temp", type=float, default=1.0, help="",dest="temp")
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parser.add_argument("--repeat_last_n", type=int, default=64, help="",dest="repeat_last_n")
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parser.add_argument("--repeat_penalty", type=float, default=1.0, help="",dest="repeat_penalty")
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parser.add_argument("-b", "--batch_size", type=int, default=8, help="",dest="n_batch")
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parser.add_argument("--keep", type=int, default=0, help="",dest="n_keep")
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parser.add_argument("-m", "--model", type=str, help="",dest="model")
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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("--interactive-start", action="store_true", help="", dest="interactive_start")
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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"
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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(
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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("--mlock", action="store_true",dest="use_mlock")
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parser.add_argument("--mtest", action="store_true",dest="mem_test")
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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="run in interactive mode and 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("--perplexity", action="store_true", help="", dest="perplexity")
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parser.add_argument("--ignore-eos", action="store_true", help="", dest="ignore_eos")
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parser.add_argument("--n_parts", type=int, default=-1, help="", dest="n_parts")
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parser.add_argument("--random-prompt", action="store_true", help="", dest="random_prompt")
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parser.add_argument("--in-prefix", type=str, default=" ", help="", dest="input_prefix")
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parser.add_argument("--fix-prefix", type=str, default=" ", help="", dest="fix_prefix")
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parser.add_argument("--out-postfix", type=str, default="", help="", dest="output_postfix")
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parser.add_argument("--input-noecho", action="store_false", help="", dest="input_echo")
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args = parser.parse_args(argv)
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return args
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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(GptParams(gpt_params_parse()))
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0
examples/low_level_api/__init__.py
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examples/low_level_api/__init__.py
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@ -12,102 +12,182 @@ Quirks:
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You should also still be feeding the model with a "primer" prompt that
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shows it the expected format.
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"""
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import sys
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from time import time
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from os import cpu_count
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import llama_cpp
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from common import GptParams, gpt_params_parse, gpt_random_prompt
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ANSI_COLOR_RESET = "\x1b[0m"
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ANSI_COLOR_YELLOW = "\x1b[33m"
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ANSI_BOLD = "\x1b[1m"
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ANSI_COLOR_GREEN = "\x1b[32m"
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CONSOLE_COLOR_DEFAULT = ANSI_COLOR_RESET
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CONSOLE_COLOR_PROMPT = ANSI_COLOR_YELLOW
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CONSOLE_COLOR_USER_INPUT = ANSI_BOLD + ANSI_COLOR_GREEN
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# A LLaMA interactive session
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class LLaMAInteract:
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def __init__(self,
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primer: str="",
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model: str="./models/30B/ggml-model-q4_0.bin",
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instruct: bool=False,
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n_ctx: int=1024,
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seed: int=0,
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n_threads: int=8,
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antiprompt: list[str]=[],
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input_echo: bool=True,
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n_predict: int=20,
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n_keep: int=0,
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n_batch: int=8,
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repeat_last_n: int=64,
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top_k: int=50,
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top_p: float=1.,
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temp: float=1.0,
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repeat_penalty: float=1,
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init_break: bool=True,
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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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) -> None:
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def __init__(self, params: GptParams) -> None:
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# input args
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self.instruct = instruct
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self.n_threads = n_threads
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self.input_echo = input_echo
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self.n_predict = n_predict
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self.n_keep = n_keep
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self.n_batch = n_batch
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self.repeat_last_n = repeat_last_n
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self.top_k=top_k
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self.top_p=top_p
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self.temp=temp
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self.repeat_penalty=repeat_penalty
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self.init_break = init_break
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self.params = params
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if (self.params.perplexity):
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raise NotImplementedError("""************
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please use the 'perplexity' tool for perplexity calculations
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************""")
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if (self.params.embedding):
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raise NotImplementedError("""************
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please use the 'embedding' tool for embedding calculations
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************""")
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if (self.params.n_ctx > 2048):
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print(f"""warning: model does not support \
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context sizes greater than 2048 tokens ({self.params.n_ctx} \
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specified) expect poor results""", file=sys.stderr)
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if (self.params.seed <= 0):
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self.params.seed = int(time())
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print(f"seed = {self.params.seed}", file=sys.stderr)
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if (self.params.random_prompt):
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self.params.prompt = gpt_random_prompt(self.params.seed)
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# runtime args
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self.input_consumed = 0
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self.embd = []
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self.embd_inp = []
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self.n_past = 0
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self.first_antiprompt = []
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self.remaining_tokens = self.n_predict
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self.output_echo = input_echo
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self.remaining_tokens = self.params.n_predict
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self.output_echo = self.params.input_echo
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# model load
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self.lparams = llama_cpp.llama_context_default_params()
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self.lparams.n_ctx = n_ctx
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self.lparams.seed = seed
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self.ctx = llama_cpp.llama_init_from_file(model.encode("utf8"), self.lparams)
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self.lparams.n_ctx = self.params.n_ctx
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self.lparams.n_parts = self.params.n_parts
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self.lparams.seed = self.params.seed
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self.lparams.memory_f16 = self.params.memory_f16
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self.lparams.use_mlock = self.params.use_mlock
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self.ctx = llama_cpp.llama_init_from_file(self.params.model.encode("utf8"), self.lparams)
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if (self.ctx == 0):
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raise RuntimeError(f"error: failed to load model '{self.params.model}'")
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print(file=sys.stderr)
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print(f"system_info: n_threads = {self.params.n_threads} / {cpu_count()} \
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| {llama_cpp.llama_print_system_info().decode('utf8')}", file=sys.stderr)
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# determine the required inference memory per token:
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tmp = [0, 1, 2, 3]
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llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
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# determine newline token
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self.llama_token_newline = self._tokenize("\n", False)
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self.inp_prefix = self._tokenize(instruct_inp_prefix)
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self.inp_suffix = self._tokenize(instruct_inp_suffix, False)
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# add instruction as antiprompt
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if (self.instruct):
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self.first_antiprompt.append(self._tokenize(instruct_inp_prefix.strip(), False))
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# primer feed
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if (len(primer) > 0):
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self.embd_inp += self._tokenize(primer)
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# number of tokens to keep when resetting context
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if (self.n_keep < 0 or self.n_keep > len(self.embd_inp) or self.instruct):
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self.n_keep = len(self.embd_inp)
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if (self.params.mem_test):
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tmp = [0, 1, 2, 3]
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llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
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llama_cpp.llama_print_timings(self.ctx)
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self.exit()
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return
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# create internal context
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self.n_ctx = llama_cpp.llama_n_ctx(self.ctx)
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self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
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# Add a space in front of the first character to match OG llama tokenizer behavior
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self.params.prompt = " " + self.params.prompt
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# tokenize the prompt
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self.embd_inp = self._tokenize(self.params.prompt)
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if (len(self.embd_inp) > self.params.n_ctx - 4):
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raise RuntimeError(f"error: prompt is too long ({len(self.embd_inp)} tokens, max {self.params.n_ctx - 4})")
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# number of tokens to keep when resetting context
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if (self.params.n_keep < 0 or self.params.n_keep > len(self.embd_inp) or self.params.instruct):
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self.params.n_keep = len(self.embd_inp)
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self.inp_prefix = self._tokenize(self.params.instruct_inp_prefix)
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self.inp_suffix = self._tokenize(self.params.instruct_inp_suffix, False)
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# in instruct mode, we inject a prefix and a suffix to each input by the user
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if (self.params.instruct):
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self.params.interactive_start = True
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self.first_antiprompt.append(self._tokenize(self.params.instruct_inp_prefix.strip(), False))
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# enable interactive mode if reverse prompt or interactive start is specified
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if (len(self.params.antiprompt) != 0 or self.params.interactive_start):
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self.params.interactive = True
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# determine newline token
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self.llama_token_newline = self._tokenize("\n", False)
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if (self.params.verbose_prompt):
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print(f"""
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prompt: '{self.params.prompt}'
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number of tokens in prompt = {len(self.embd_inp)}""", file=sys.stderr)
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for i in range(len(self.embd_inp)):
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print(f"{self.embd_inp[i]} -> '{llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i])}'", file=sys.stderr)
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if (self.params.n_keep > 0):
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print("static prompt based on n_keep: '")
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for i in range(self.params.n_keep):
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print(llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i]), file=sys.stderr)
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print("'", file=sys.stderr)
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print(file=sys.stderr)
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if (self.params.interactive):
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print("interactive mode on.", file=sys.stderr)
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if (len(self.params.antiprompt) > 0):
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for antiprompt in self.params.antiprompt:
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print(f"Reverse prompt: '{antiprompt}'", file=sys.stderr)
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if len(self.params.input_prefix) > 0:
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print(f"Input prefix: '{self.params.input_prefix}'", file=sys.stderr)
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print(f"""sampling: temp = {self.params.temp},\
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top_k = {self.params.top_k},\
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top_p = {self.params.top_p},\
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repeat_last_n = {self.params.repeat_last_n},\
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repeat_penalty = {self.params.repeat_penalty}
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generate: n_ctx = {self.n_ctx}, \
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n_batch = {self.params.n_batch}, \
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n_predict = {self.params.n_predict}, \
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n_keep = {self.params.n_keep}
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""", file=sys.stderr)
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# determine antiprompt tokens
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for i in antiprompt:
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for i in self.params.antiprompt:
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self.first_antiprompt.append(self._tokenize(i, False))
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self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
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if (params.interactive):
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print("""== Running in interactive mode. ==
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- Press Ctrl+C to interject at any time.
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- Press Return to return control to LLaMa.
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- If you want to submit another line, end your input in '\\'.
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""", file=sys.stderr)
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self.set_color(CONSOLE_COLOR_PROMPT)
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# tokenize a prompt
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def _tokenize(self, prompt, bos=True):
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_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
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_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), _arr, len(_arr), bos)
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return _arr[:_n]
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# if an antiprompt is present
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def use_antiprompt(self):
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return len(self.first_antiprompt) > 0
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def set_color(self, c):
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if (self.params.use_color):
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print(c)
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# generate tokens
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def generate(self):
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while self.remaining_tokens > 0 or self.use_antiprompt():
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while self.remaining_tokens > 0 or self.params.interactive:
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# predict
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if len(self.embd) > 0:
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# infinite text generation via context swapping
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@ -115,8 +195,8 @@ class LLaMAInteract:
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# - take the n_keep first tokens from the original prompt (via n_past)
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# - take half of the last (n_ctx - n_keep) tokens and recompute the logits in a batch
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if (self.n_past + len(self.embd) > self.n_ctx):
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n_left = self.n_past - self.n_keep
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self.n_past = self.n_keep
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n_left = self.n_past - self.params.n_keep
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self.n_past = self.params.n_keep
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# insert n_left/2 tokens at the start of embd from last_n_tokens
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_insert = self.last_n_tokens[
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@ -125,7 +205,7 @@ class LLaMAInteract:
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self.embd = _insert + self.embd
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if (llama_cpp.llama_eval(
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self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.n_threads
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self.ctx, (llama_cpp.llama_token * len(self.embd))(*self.embd), len(self.embd), self.n_past, self.params.n_threads
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) != 0):
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raise Exception("Failed to llama_eval!")
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@ -133,24 +213,28 @@ class LLaMAInteract:
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self.embd = []
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if len(self.embd_inp) <= self.input_consumed:
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# out of user input, sample next token
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_arr = self.last_n_tokens[-min(self.repeat_last_n, self.n_past):]
|
||||
|
||||
#TODO: self.params.ignore_eos
|
||||
|
||||
_arr = self.last_n_tokens[-min(self.params.repeat_last_n, self.n_past):]
|
||||
id = llama_cpp.llama_sample_top_p_top_k(
|
||||
self.ctx,
|
||||
(llama_cpp.llama_token * len(_arr))(*_arr),
|
||||
len(_arr),
|
||||
self.top_k,
|
||||
self.top_p,
|
||||
self.temp,
|
||||
self.repeat_penalty,
|
||||
self.params.top_k,
|
||||
self.params.top_p,
|
||||
self.params.temp,
|
||||
self.params.repeat_penalty,
|
||||
)
|
||||
self.last_n_tokens.pop(0)
|
||||
self.last_n_tokens.append(id)
|
||||
|
||||
# replace end of text token with newline token when in interactive mode
|
||||
if (id == llama_cpp.llama_token_eos() and self.use_antiprompt() and not self.instruct):
|
||||
if (id == llama_cpp.llama_token_eos() and self.params.interactive and not self.params.instruct):
|
||||
id = self.llama_token_newline[0]
|
||||
# tokenize and inject first reverse prompt
|
||||
self.embd_inp += self.first_antiprompt[0]
|
||||
if (self.use_antiprompt()):
|
||||
# tokenize and inject first reverse prompt
|
||||
self.embd_inp += self.first_antiprompt[0]
|
||||
|
||||
# add it to the context
|
||||
self.embd.append(id)
|
||||
|
|
@ -162,7 +246,7 @@ class LLaMAInteract:
|
|||
self.remaining_tokens -= 1
|
||||
else:
|
||||
# output to console if input echo is on
|
||||
self.output_echo = self.input_echo
|
||||
self.output_echo = self.params.input_echo
|
||||
|
||||
# some user input remains from prompt or interaction, forward it to processing
|
||||
while len(self.embd_inp) > self.input_consumed:
|
||||
|
|
@ -170,7 +254,7 @@ class LLaMAInteract:
|
|||
self.last_n_tokens.pop(0)
|
||||
self.last_n_tokens.append(self.embd_inp[self.input_consumed])
|
||||
self.input_consumed += 1
|
||||
if len(self.embd) >= self.n_batch:
|
||||
if len(self.embd) >= self.params.n_batch:
|
||||
break
|
||||
|
||||
# display tokens
|
||||
|
|
@ -178,7 +262,11 @@ class LLaMAInteract:
|
|||
for id in self.embd:
|
||||
yield id
|
||||
|
||||
if (len(self.embd_inp) <= self.input_consumed):
|
||||
# reset color to default if we there is no pending user input
|
||||
if (self.params.input_echo and len(self.embd_inp) == self.input_consumed):
|
||||
self.set_color(CONSOLE_COLOR_DEFAULT)
|
||||
|
||||
if (self.params.interactive and len(self.embd_inp) <= self.input_consumed):
|
||||
# if antiprompt is present, stop
|
||||
if (self.use_antiprompt()):
|
||||
if True in [
|
||||
|
|
@ -188,26 +276,36 @@ class LLaMAInteract:
|
|||
break
|
||||
|
||||
# if we are using instruction mode, and we have processed the initial prompt
|
||||
if (self.init_break):
|
||||
if (self.n_past > 0 and self.params.interactive_start):
|
||||
break
|
||||
|
||||
# if end of generation
|
||||
# end of text token
|
||||
if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos():
|
||||
if (not self.params.instruct):
|
||||
for i in " [end of text]\n":
|
||||
yield i
|
||||
break
|
||||
|
||||
# respect n_predict even if antiprompt is present
|
||||
if (self.use_antiprompt() and self.remaining_tokens <= 0 and self.n_predict != -1):
|
||||
if not self.instruct:
|
||||
if (self.params.interactive and self.remaining_tokens <= 0 and self.params.n_predict != -1):
|
||||
# If we arent in instruction mode, fix the current generation by appending the antiprompt.
|
||||
# Makes it so if chat ends prematurely you dont append the AI's text etc.
|
||||
if not self.params.instruct:
|
||||
self.embd_inp += self.first_antiprompt[0]
|
||||
self.n_remain = self.params.n_predict
|
||||
break
|
||||
|
||||
self.init_break = False
|
||||
self.params.interactive_start = False
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, type, value, tb):
|
||||
self.exit()
|
||||
|
||||
def exit(self):
|
||||
llama_cpp.llama_free(self.ctx)
|
||||
self.set_color(CONSOLE_COLOR_DEFAULT)
|
||||
|
||||
# return past text
|
||||
def past(self):
|
||||
|
|
@ -216,18 +314,51 @@ class LLaMAInteract:
|
|||
|
||||
# write input
|
||||
def input(self, prompt: str):
|
||||
if (self.instruct and self.last_n_tokens[-len(self.inp_prefix):] != self.inp_prefix):
|
||||
if (self.params.instruct and self.last_n_tokens[-len(self.inp_prefix):] != self.inp_prefix):
|
||||
self.embd_inp += self.inp_prefix
|
||||
self.embd_inp += self._tokenize(prompt)
|
||||
if (self.instruct):
|
||||
if (self.params.instruct):
|
||||
self.embd_inp += self.inp_suffix
|
||||
|
||||
# write output
|
||||
def output(self):
|
||||
self.remaining_tokens = self.n_predict
|
||||
self.remaining_tokens = self.params.n_predict
|
||||
for id in self.generate():
|
||||
yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8")
|
||||
|
||||
# read user input
|
||||
def read_input(self):
|
||||
out = ""
|
||||
while (t := input()).endswith("\\"):
|
||||
out += t[:-1] + "\n"
|
||||
return out + t + "\n"
|
||||
|
||||
# interactive mode
|
||||
def interact(self):
|
||||
for i in self.output():
|
||||
print(i,end="",flush=True)
|
||||
self.params.input_echo = False
|
||||
|
||||
while self.params.interactive:
|
||||
self.set_color(CONSOLE_COLOR_USER_INPUT)
|
||||
if (self.params.instruct):
|
||||
print('\n> ', end="")
|
||||
self.input(self.read_input())
|
||||
else:
|
||||
print(self.params.input_prefix, end="")
|
||||
self.input(f"{self.params.input_prefix}{self.read_input()}{self.params.output_postfix}")
|
||||
print(self.params.output_postfix,end="")
|
||||
self.set_color(CONSOLE_COLOR_DEFAULT)
|
||||
|
||||
try:
|
||||
for i in self.output():
|
||||
print(i,end="",flush=True)
|
||||
except KeyboardInterrupt:
|
||||
self.set_color(CONSOLE_COLOR_DEFAULT)
|
||||
if not self.params.instruct:
|
||||
print(self.params.fix_prefix,end="")
|
||||
self.input(self.params.fix_prefix)
|
||||
|
||||
if __name__ == "__main__":
|
||||
from datetime import datetime
|
||||
|
||||
|
|
@ -252,41 +383,12 @@ The transcript only includes text, it does not include markup like HTML and Mark
|
|||
{USER_NAME}: Name a color.
|
||||
{AI_NAME}: Blue
|
||||
{USER_NAME}:"""
|
||||
args = gpt_params_parse()
|
||||
params = GptParams(args)
|
||||
|
||||
print("Loading model...")
|
||||
with LLaMAInteract(prompt,
|
||||
model="./models/30B/ggml-model-q4_0.bin",
|
||||
n_ctx=2048,
|
||||
antiprompt=[f"\n{USER_NAME}:"],
|
||||
repeat_last_n=256,
|
||||
n_predict=2048,
|
||||
temp=0.7, top_p=0.5, top_k=40, repeat_penalty=1.17647
|
||||
) as m:
|
||||
print("Loaded model!")
|
||||
if (args.file):
|
||||
with open(args.file) as f:
|
||||
params.prompt = f.read()
|
||||
|
||||
for i in m.output():
|
||||
print(i,end="",flush=True)
|
||||
m.input_echo = False
|
||||
|
||||
def inp():
|
||||
out = ""
|
||||
while (t := input()).endswith("\\"):
|
||||
out += t[:-1] + "\n"
|
||||
return out + t + "\n"
|
||||
|
||||
while True:
|
||||
if (m.instruct):
|
||||
print('\n> ', end="")
|
||||
m.input(inp())
|
||||
else:
|
||||
print(f" ", end="")
|
||||
m.input(f" {inp()}{AI_NAME}:")
|
||||
print(f"{AI_NAME}: ",end="")
|
||||
|
||||
try:
|
||||
for i in m.output():
|
||||
print(i,end="",flush=True)
|
||||
except KeyboardInterrupt:
|
||||
if not m.instruct:
|
||||
print(f"\n{USER_NAME}:",end="")
|
||||
m.input(f"\n{USER_NAME}:")
|
||||
with LLaMAInteract() as m:
|
||||
m.interact()
|
||||
|
|
|
|||
Loading…
Reference in a new issue