src/models/opt.py

"""
OPT モデル実装

Meta OPT-125Mの実装を提供する
"""
from typing import List, Tuple

import torch
from transformers import OPTForCausalLM, GPT2Tokenizer

from .base import BaseLanguageModel, ModelConfig


# OPT-125M設定
OPT_125M_CONFIG = ModelConfig(
    name="OPT-125M",
    model_id="facebook/opt-125m",
    embedding_dim=768,
    vocab_size=50272,
)


class OPTModel(BaseLanguageModel):
    """
    OPTモデルの実装

    Meta OPTをラップしBaseLanguageModelインターフェースを実装
    """

    # 出力ノイズの倍率
    LOGITS_NOISE_SCALE = 10.0

    def load(self) -> None:
        """モデルとトークナイザーをロード"""
        if self._is_loaded:
            return

        try:
            self._model = OPTForCausalLM.from_pretrained(self._config.model_id)
            # OPTは独自のトークナイザーを持つが、GPT-2互換も可能
            self._tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
            self._model.eval()
            self._is_loaded = True
        except Exception as e:
            raise RuntimeError(f"Failed to load model {self._config.model_id}: {e}")

    def forward_with_noise(
        self, noise: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """ノイズを入力として順伝播を実行"""
        if not self._is_loaded:
            raise RuntimeError("Model not loaded. Call load() first.")

        with torch.no_grad():
            outputs = self._model(inputs_embeds=noise)
            logits = outputs.logits

            logits_noise = (
                torch.randn_like(logits) * logits.std() * self.LOGITS_NOISE_SCALE
            )
            corrupted_logits = logits + logits_noise

        return logits, corrupted_logits

    def decode_indices(self, indices: List[int]) -> List[str]:
        """トークンインデックスをデコード"""
        if not self._is_loaded:
            raise RuntimeError("Model not loaded. Call load() first.")

        return [self._tokenizer.decode([i]) for i in indices]