generate.py

import os
import sys
import json
import re

import fire
import gradio as gr
import torch
import transformers
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer

from utils.callbacks import Iteratorize, Stream
from utils.prompter import Prompter

if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

try:
    if torch.backends.mps.is_available():
        device = "mps"
except:  # noqa: E722
    pass


def main(
    load_8bit: bool = False,
    base_model: str = "",
    lora_weights: str = "tloen/alpaca-lora-7b",
    prompt_template: str = "",  # The prompt template to use, will default to alpaca.
    server_name: str = "0.0.0.0",  # Allows to listen on all interfaces by providing '0.
    share_gradio: bool = False,
):
    base_model = base_model or os.environ.get("BASE_MODEL", "")
    assert (
        base_model
    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"

    prompter = Prompter(prompt_template)
    tokenizer = LlamaTokenizer.from_pretrained(base_model)
    if device == "cuda":
        model = LlamaForCausalLM.from_pretrained(
            base_model,
            load_in_8bit=load_8bit,
            torch_dtype=torch.float16,
            device_map="auto",
        )
        model = PeftModel.from_pretrained(
            model,
            lora_weights,
            torch_dtype=torch.float16,
        )
    elif device == "mps":
        model = LlamaForCausalLM.from_pretrained(
            base_model,
            device_map={"": device},
            torch_dtype=torch.float16,
        )
        model = PeftModel.from_pretrained(
            model,
            lora_weights,
            device_map={"": device},
            torch_dtype=torch.float16,
        )
    else:
        model = LlamaForCausalLM.from_pretrained(
            base_model, device_map={"": device}, low_cpu_mem_usage=True
        )
        model = PeftModel.from_pretrained(
            model,
            lora_weights,
            device_map={"": device},
        )

    # unwind broken decapoda-research config
    model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
    model.config.bos_token_id = 1
    model.config.eos_token_id = 2

    if not load_8bit:
        model.half()  # seems to fix bugs for some users.

    model.eval()
    if torch.__version__ >= "2" and sys.platform != "win32":
        model = torch.compile(model)

    def evaluate(
        instruction,
        input=None,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        max_new_tokens=128,
        stream_output=False,
        **kwargs,
    ):
        prompt = prompter.generate_prompt(instruction, input)
        inputs = tokenizer(prompt, return_tensors="pt")
        input_ids = inputs["input_ids"].to(device)
        generation_config = GenerationConfig(
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            num_beams=num_beams,
            **kwargs,
        )

        generate_params = {
            "input_ids": input_ids,
            "generation_config": generation_config,
            "return_dict_in_generate": True,
            "output_scores": True,
            "max_new_tokens": max_new_tokens,
        }

        if stream_output:
            # Stream the reply 1 token at a time.
            # This is based on the trick of using 'stopping_criteria' to create an iterator,
            # from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.

            def generate_with_callback(callback=None, **kwargs):
                kwargs.setdefault(
                    "stopping_criteria", transformers.StoppingCriteriaList()
                )
                kwargs["stopping_criteria"].append(
                    Stream(callback_func=callback)
                )
                with torch.no_grad():
                    model.generate(**kwargs)

            def generate_with_streaming(**kwargs):
                return Iteratorize(
                    generate_with_callback, kwargs, callback=None
                )

            with generate_with_streaming(**generate_params) as generator:
                for output in generator:
                    # new_tokens = len(output) - len(input_ids[0])
                    decoded_output = tokenizer.decode(output)

                    if output[-1] in [tokenizer.eos_token_id]:
                        break

                    yield prompter.get_response(decoded_output)
            return  # early return for stream_output

    read_file = sys.argv[1]
    output_file = sys.argv[2]

    with open(read_file, "r") as f:
        data = json.load(f)
    for i in data:
        with open(output_file, "a") as output:
            result = evaluate(i["instruction"], i["input"]).replace("\n", " ")
            text = re.sub(r'\s+', ' ', text)
            output.write(result + "\n")

if __name__ == "__main__":
    fire.Fire(main)