code/Abforge_Training/verl_proj/src/dapo.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 verl import DataProto
from verl.utils.reward_score import _default_compute_score
import torch
from collections import defaultdict


class DAPORewardManager:
    """The reward manager.
    """

    def __init__(self,
                 tokenizer,
                 num_examine,
                 compute_score=None,
                 reward_fn_key='data_source',
                 max_resp_len=None,
                 overlong_buffer_cfg=None) -> None:
        self.tokenizer = tokenizer
        self.num_examine = num_examine  # the number of batches of decoded responses to print to the console
        self.compute_score = compute_score or _default_compute_score
        self.reward_fn_key = reward_fn_key
        self.overlong_buffer_cfg = overlong_buffer_cfg
        self.max_resp_len = max_resp_len

        if self.overlong_buffer_cfg is not None:
            assert self.max_resp_len is not None, f"max_resp_len must be provided if {overlong_buffer_cfg=}, but got None"

    def __call__(self, data: DataProto, return_dict: bool = False):
        """We will expand this function gradually based on the available datasets"""

        # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn
        if 'rm_scores' in data.batch.keys():
            if return_dict:
                return {"reward_tensor": data.batch['rm_scores']}
            else:
                return data.batch['rm_scores']

        reward_tensor = torch.zeros_like(data.batch['responses'], dtype=torch.float32)
        reward_extra_info = defaultdict(list)

        already_print_data_sources = {}

        for i in range(len(data)):
            data_item = data[i]  # DataProtoItem

            prompt_ids = data_item.batch['prompts']

            prompt_length = prompt_ids.shape[-1]

            valid_prompt_length = data_item.batch['attention_mask'][:prompt_length].sum()
            valid_prompt_ids = prompt_ids[-valid_prompt_length:]

            response_ids = data_item.batch['responses']
            valid_response_length = data_item.batch['attention_mask'][prompt_length:].sum()
            valid_response_ids = response_ids[:valid_response_length]

            # decode
            prompt_str = self.tokenizer.decode(valid_prompt_ids, skip_special_tokens=True)
            response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True)
            eos_token = self.tokenizer.eos_token
            if response_str.endswith(eos_token):
                response_str = response_str[:-len(eos_token)]

            ground_truth = data_item.non_tensor_batch['reward_model']['ground_truth']

            data_source = data_item.non_tensor_batch[self.reward_fn_key]

            extra_info = data_item.non_tensor_batch.get('extra_info', None)

            result = self.compute_score(
                data_source=data_source,
                solution_str=response_str,
                ground_truth=ground_truth,
                extra_info=extra_info,
            )

            score: float
            if isinstance(result, dict):
                score = result["score"]
                # Store the information including original reward
                for key, value in result.items():
                    reward_extra_info[key].append(value)
            else:
                score = result

            reward = score

            if self.overlong_buffer_cfg.enable:
                overlong_buffer_len = self.overlong_buffer_cfg.len
                expected_len = self.max_resp_len - overlong_buffer_len
                exceed_len = valid_response_length - expected_len
                overlong_penalty_factor = self.overlong_buffer_cfg.penalty_factor
                overlong_reward = min(-exceed_len / overlong_buffer_len * overlong_penalty_factor, 0)
                reward += overlong_reward
                if self.overlong_buffer_cfg.log:
                    reward_extra_info["overlong_reward"].append(overlong_reward)
                    reward_extra_info["overlong"].append(overlong_reward < 0)

            reward_tensor[i, valid_response_length - 1] = reward

            if data_source not in already_print_data_sources:
                already_print_data_sources[data_source] = 0

            if already_print_data_sources[data_source] < self.num_examine:
                already_print_data_sources[data_source] += 1
                print("[prompt]", prompt_str)
                print("[response]", response_str)
                print("[ground_truth]", ground_truth)
                if isinstance(result, dict):
                    for key, value in result.items():
                        print(f"[{key}]", value)
                else:
                    print(f"[score]", score)

        if return_dict:
            return {
                "reward_tensor": reward_tensor,
                "reward_extra_info": reward_extra_info,
            }
        else:
            return reward_tensor