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7ae83df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | # 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.
import importlib.util
import os
import sys
from abc import ABC, abstractmethod
from collections import defaultdict
from functools import partial
from typing import Callable, Optional, Tuple, TypedDict
import torch
from transformers import PreTrainedTokenizer
from ...protocol import DataProto
from .config import RewardConfig
class RewardInput(TypedDict):
response: str
response_length: int
ground_truth: str
class RewardScore(TypedDict):
overall: float
format: Optional[float]
accuracy: Optional[float]
SequentialRewardFunction = Callable[[RewardInput], RewardScore]
BatchRewardFunction = Callable[[list[RewardInput]], list[RewardScore]]
class FunctionRewardManager(ABC):
"""Reward manager for rule-based reward."""
def __init__(self, config: RewardConfig, tokenizer: PreTrainedTokenizer):
if config.reward_function is None:
raise ValueError("Reward function is not provided.")
if not os.path.exists(config.reward_function):
raise FileNotFoundError(f"Reward function file {config.reward_function} not found.")
spec = importlib.util.spec_from_file_location("custom_reward_fn", config.reward_function)
module = importlib.util.module_from_spec(spec)
try:
sys.modules["custom_reward_fn"] = module
spec.loader.exec_module(module)
except Exception as e:
raise RuntimeError(f"Failed to load reward function: {e}")
if not hasattr(module, config.reward_function_name):
raise AttributeError(f"Module {module} does not have function {config.reward_function_name}.")
reward_fn = getattr(module, config.reward_function_name)
print(f"Using reward function `{config.reward_function_name}` from `{config.reward_function}`.")
self.reward_fn = partial(reward_fn, **config.reward_function_kwargs)
self.config = config
self.tokenizer = tokenizer
@abstractmethod
def compute_reward(self, data: DataProto) -> Tuple[torch.Tensor, dict[str, list[float]]]:
"""Compute reward for a batch of data."""
...
class SequentialFunctionRewardManager(FunctionRewardManager):
reward_fn: SequentialRewardFunction
def compute_reward(self, data: DataProto) -> Tuple[torch.Tensor, dict[str, list[float]]]:
reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
reward_metrics = defaultdict(list)
response_ids = data.batch["responses"]
response_length = torch.sum(data.batch["response_mask"], dim=-1)
for i in range(len(data)):
cur_response_length = int(response_length[i].item()) # avoid tensor indexing error
valid_response_ids = response_ids[i][:cur_response_length]
response_str = self.tokenizer.decode(
valid_response_ids, skip_special_tokens=self.config.skip_special_tokens
)
score = self.reward_fn(
{
"response": response_str,
"response_length": cur_response_length,
"ground_truth": data.non_tensor_batch["ground_truth"][i],
}
)
reward_tensor[i, cur_response_length - 1] = score["overall"]
for key, value in score.items():
reward_metrics[key].append(value)
return reward_tensor, reward_metrics
class BatchFunctionRewardManager(FunctionRewardManager):
reward_fn: BatchRewardFunction
def compute_reward(self, data: DataProto) -> Tuple[torch.Tensor, dict[str, list[float]]]:
reward_inputs = []
response_ids = data.batch["responses"]
response_length = torch.sum(data.batch["response_mask"], dim=-1)
for i in range(len(data)):
cur_response_length = int(response_length[i].item()) # avoid tensor indexing error
valid_response_ids = response_ids[i][:cur_response_length]
response_str = self.tokenizer.decode(
valid_response_ids, skip_special_tokens=self.config.skip_special_tokens
)
reward_inputs.append(
{
"response": response_str,
"response_length": cur_response_length,
"ground_truth": data.non_tensor_batch["ground_truth"][i],
"data_type": data.non_tensor_batch["data_type"][i],
"problem_type": data.non_tensor_batch["problem_type"][i],
"problem": data.non_tensor_batch["problem_reserved_text"][i],
"problem_id": data.non_tensor_batch["problem_id"][i],
}
)
# print(data)
# print("\n\n\n\n\n\n\n\n\n")
# print(reward_inputs)
scores = self.reward_fn(reward_inputs)
reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
reward_metrics = defaultdict(list)
for i, score in enumerate(scores):
cur_response_length = int(response_length[i].item()) # avoid tensor indexing error
reward_tensor[i, cur_response_length - 1] = score["overall"]
for key, value in score.items():
reward_metrics[key].append(value)
return reward_tensor, reward_metrics
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