File size: 4,522 Bytes
bcdf9fa |
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 |
# Copyright 2025 Individual Contributor: Mert Unsal
#
# 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
import torch
from verl import DataProto
class BatchRewardManager:
def __init__(self, tokenizer, num_examine, compute_score, reward_fn_key="data_source", **reward_kwargs):
self.tokenizer = tokenizer
self.num_examine = num_examine
self.compute_score = compute_score
self.reward_fn_key = reward_fn_key
self.reward_kwargs = reward_kwargs
def verify(self, data):
prompt_ids = data.batch["prompts"]
response_ids = data.batch["responses"]
attention_mask = data.batch["attention_mask"]
prompt_len = prompt_ids.shape[-1]
valid_response_lengths = attention_mask[:, prompt_len:].sum(dim=-1)
responses_str = []
for i in range(len(data)):
valid_len = valid_response_lengths[i]
valid_response_ids = response_ids[i][:valid_len]
response_str = self.tokenizer.decode(valid_response_ids, skip_special_tokens=True)
responses_str.append(response_str)
ground_truths = [item.non_tensor_batch["reward_model"].get("ground_truth", None) for item in data]
data_sources = data.non_tensor_batch[self.reward_fn_key]
extras = data.non_tensor_batch.get("extra_info", [None] * len(data))
scores = self.compute_score(
data_sources=data_sources,
solution_strs=responses_str,
ground_truths=ground_truths,
extra_infos=extras,
**self.reward_kwargs,
)
return scores
def __call__(self, data: DataProto, return_dict=False):
# 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)
prompt_ids = data.batch["prompts"]
prompt_len = prompt_ids.shape[-1]
attention_mask = data.batch["attention_mask"]
valid_response_lengths = attention_mask[:, prompt_len:].sum(dim=-1)
data_sources = data.non_tensor_batch[self.reward_fn_key]
scores = self.verify(data)
rewards = []
already_printed = {}
for i in range(len(data)):
length = valid_response_lengths[i].item()
score = scores[i]
if isinstance(score, dict):
reward = score["score"]
for key, value in score.items():
reward_extra_info[key].append(value)
else:
reward = score
rewards.append(reward)
reward_tensor[i, length - 1] = reward
data_source = data_sources[i]
if already_printed.get(data_source, 0) < self.num_examine:
response_str = self.tokenizer.decode(data.batch["responses"][i][:length], skip_special_tokens=True)
prompt_str = self.tokenizer.decode(data.batch["prompts"][i], skip_special_tokens=True)
ground_truth = data[i].non_tensor_batch["reward_model"].get("ground_truth", None)
print("[prompt]", prompt_str)
print("[response]", response_str)
print("[ground_truth]", ground_truth)
print("[score]", scores[i])
already_printed[data_source] = already_printed.get(data_source, 0) + 1
data.batch["acc"] = torch.tensor(rewards, dtype=torch.float32, device=prompt_ids.device)
if return_dict:
return {"reward_tensor": reward_tensor, "reward_extra_info": reward_extra_info}
else:
return reward_tensor
|