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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | # 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
import torch
from verl import DataProto
from verl.utils.reward_score import _default_compute_score
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("[score]", score)
if return_dict:
return {
"reward_tensor": reward_tensor,
"reward_extra_info": reward_extra_info,
}
else:
return reward_tensor
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