custom.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from collections import defaultdict
from typing import Any, Callable, Dict, Tuple, TypedDict

import torch
from transformers import PreTrainedTokenizer

from ...protocol import DataProto
from ...utils.reward_score import math_compute_score, r1v_compute_score


class RewardScore(TypedDict):
    overall: float
    format: float
    accuracy: float


class CustomRewardManager:
    def __init__(self, tokenizer: PreTrainedTokenizer, compute_score: str):
        self.tokenizer = tokenizer
        if compute_score == "math":
            self.compute_score: Callable[[str, str], RewardScore] = math_compute_score
        elif compute_score == "r1v":
            self.compute_score: Callable[[str, str], RewardScore] = r1v_compute_score
        else:
            raise NotImplementedError()

    def __call__(self, data: DataProto) -> Tuple[torch.Tensor, Dict[str, Any]]:
        reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
        reward_metrics = defaultdict(list)
        for i in range(len(data)):
            data_item = data[i]  # DataProtoItem
            response_ids = data_item.batch["responses"]
            response_mask = data_item.batch["response_mask"]
            valid_response_length = response_mask.sum()
            valid_response_ids = response_ids[:valid_response_length]

            response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True)
            ground_truth = data_item.non_tensor_batch["ground_truth"]

            score = self.compute_score(response_str, ground_truth)
            reward_tensor[i, valid_response_length - 1] = score["overall"]
            for key, value in score.items():
                reward_metrics[key].append(value)

        return reward_tensor, reward_metrics