FlexGen のインストールと動作確認（大規模言語モデル，チャットボット）（Python，PyTorch を使用）（Windows 上）

"""Run a chatBOT with FlexGen and OPT models."""
import argparse
import sys
import win32com.client
import flexgen
from flexgen.flex_opt import (Policy, OptLM, ExecutionEnv, CompressionConfig, str2bool)
from transformers import AutoTokenizer

speech = win32com.client.Dispatch("Sapi.SpVoice")

def main(args):
    # Initialize environment
    env = ExecutionEnv.create(args.offload_dir)

    # Offloading policy
    policy = Policy(1, 1,
                    args.percent[0], args.percent[1],
                    args.percent[2], args.percent[3],
                    args.percent[4], args.percent[5],
                    overlap=True, sep_layer=True, pin_weight=args.pin_weight,
                    cpu_cache_compute=False, attn_sparsity=1.0,
                    compress_weight=args.compress_weight,
                    comp_weight_config=CompressionConfig(
                        num_bits=4, group_size=64,
                        group_dim=0, symmetric=False),
                    compress_cache=args.compress_cache,
                    comp_cache_config=CompressionConfig(
                        num_bits=4, group_size=64,
                        group_dim=2, symmetric=False))

    # Model
    print("Initialize...")
    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-30b", padding_side="left")
    tokenizer.add_bos_token = False
    stop = tokenizer("\n").input_ids[0]

    model = OptLM(args.model, env, args.path, policy)

    context = (
        "A chat between a curious human and a knowledgeable artificial intelligence assistant.\n"
        "Human: Hello! What can you do?\n"
        "Assistant: As an AI assistant, I can answer questions and chat with you.\n"
    )

    # Chat
    print(context, end="")
    while True:
        inp = input("Human: ")
        if not inp:
            print("exit...")
            break

        speech.Speak(inp)

        context += "Human: " + inp + "\n"
        inputs = tokenizer([context])
        output_ids = model.generate(
            inputs.input_ids,
            do_sample=True,
            temperature=0.7,
            max_new_tokens=96,
            stop=stop)
        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
        try:
            index = outputs.index("\n", len(context))
        except ValueError:
            outputs += "\n"
            index = outputs.index("\n", len(context))
        
        outputs = outputs[:index + 1]
        print(outputs[len(context):], end="")
        speech.Speak(outputs[len(context):])
        context = outputs

    # TODO: optimize the performance by reusing context cache and reducing redundant computation.

    # Shutdown
    env.close_copy_threads()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", type=str, default="facebook/opt-6.7b",
        help="The model name.")
    parser.add_argument("--path", type=str, default="~/opt_weights",
        help="The path to the model weights. If there are no cached weights, "
             "FlexGen will automatically download them from HuggingFace.")
    parser.add_argument("--offload-dir", type=str, default="~/flexgen_offload_dir",
        help="The directory to offload tensors. ")
    parser.add_argument("--percent", nargs="+", type=int,
        default=[100, 0, 100, 0, 100, 0],
        help="Six numbers. They are "
         "the percentage of weight on GPU, "
         "the percentage of weight on CPU, "
         "the percentage of attention cache on GPU, "
         "the percentage of attention cache on CPU, "
         "the percentage of activations on GPU, "
         "the percentage of activations on CPU")
    parser.add_argument("--pin-weight", type=str2bool, nargs="?",
        const=True, default=True)
    parser.add_argument("--compress-weight", action="store_true",
        help="Whether to compress weight.")
    parser.add_argument("--compress-cache", action="store_true",
        help="Whether to compress cache.")
    args = parser.parse_args()

    assert len(args.percent) == 6

    main(args)

FlexGen のインストールと動作確認（大規模言語モデル，チャットボット）（Python，PyTorch を使用）（Windows 上）

FlexGen

前準備

Git のインストール（Windows 上）

Python のインストール（Windows 上）

Build Tools for Visual Studio 2022，NVIDIA ドライバ，NVIDIA CUDA ツールキット，NVIDIA cuDNN のインストール（Windows 上）

PyTorch のインストール（Windows 上）

FlexGen のインストール

チャットボットを動かす（FlexGen，OPT を使用）（Windows 上）

チャットボットに対話，音声合成を行う Python プログラム（FlexGen，OPT を使用）（Windows 上）