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# 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 os
from typing import List, Union
import pandas as pd
import torch
from torch.utils.data import Dataset
from verl.utils import hf_tokenizer
def download_files_distributed(download_fn):
import torch.distributed
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
# download files
download_fn()
torch.distributed.barrier()
else:
# download anyway
download_fn()
class RMDataset(Dataset):
def __init__(
self,
parquet_files: Union[str, List[str]],
tokenizer,
prompt_key="prompt",
chosen_key="chosen",
rejected_key="rejected",
max_length=1024,
add_eos=True,
cache_dir="~/.cache/verl/rm",
):
if not isinstance(parquet_files, List):
parquet_files = [parquet_files]
self.parquet_files = parquet_files
self.cache_dir = os.path.expanduser(cache_dir)
if isinstance(tokenizer, str):
tokenizer = hf_tokenizer(tokenizer)
self.tokenizer = tokenizer
self.prompt_key = prompt_key
self.chosen_key = chosen_key
self.rejected_key = rejected_key
self.add_eos = add_eos
self.max_length = max_length
self._download()
self._read_files_and_tokenize()
def _download(self):
def _download_files():
from verl.utils.fs import copy, is_non_local
os.makedirs(self.cache_dir, exist_ok=True)
assert os.path.exists(self.cache_dir)
for i, parquet_file in enumerate(self.parquet_files):
if is_non_local(parquet_file):
dst = os.path.join(self.cache_dir, os.path.basename(parquet_file))
if not os.path.exists(dst):
copy(src=parquet_file, dst=dst)
self.parquet_files[i] = dst
download_files_distributed(_download_files)
def _read_files_and_tokenize(self):
dataframes = []
for parquet_file in self.parquet_files:
# read parquet files and cache
dataframe = pd.read_parquet(parquet_file)
dataframes.append(dataframe)
self.dataframe = pd.concat(dataframes)
self.prompts = self.dataframe[self.prompt_key].tolist()
self.chosen_responses = self.dataframe[self.chosen_key].tolist()
self.rejected_responses = self.dataframe[self.rejected_key].tolist()
def __len__(self):
return len(self.prompts)
def _pad_to_length(self, input_ids, attention_mask):
curr_length = input_ids.shape[-1]
if curr_length < self.max_length:
input_ids = torch.cat((input_ids, torch.zeros(size=(self.max_length - curr_length,), dtype=input_ids.dtype)), dim=-1)
attention_mask = torch.cat((attention_mask, torch.zeros(size=(self.max_length - curr_length,), dtype=attention_mask.dtype)), dim=-1)
elif curr_length > self.max_length:
input_ids = input_ids[: self.max_length]
attention_mask = attention_mask[: self.max_length]
return input_ids, attention_mask
def __getitem__(self, item):
prompt = self.prompts[item]
chosen_response = self.chosen_responses[item]
rejected_response = self.rejected_responses[item]
prompt_ids = self.tokenizer(prompt, return_tensors="pt")["input_ids"][0]
chosen_response_ids = self.tokenizer(chosen_response, return_tensors="pt")["input_ids"][0]
rejected_response_ids = self.tokenizer(rejected_response, return_tensors="pt")["input_ids"][0]
if self.add_eos:
chosen_response_ids = torch.cat((chosen_response_ids, torch.tensor([self.tokenizer.eos_token_id])), dim=-1)
rejected_response_ids = torch.cat((rejected_response_ids, torch.tensor([self.tokenizer.eos_token_id])), dim=-1)
chosen_input_ids = torch.cat((prompt_ids, chosen_response_ids), dim=-1)
chosen_attention_mask = torch.ones_like(chosen_input_ids)
rejected_input_ids = torch.cat((prompt_ids, rejected_response_ids), dim=-1)
rejected_attention_mask = torch.ones_like(rejected_input_ids)
chosen_input_ids, chosen_attention_mask = self._pad_to_length(chosen_input_ids, chosen_attention_mask)
rejected_input_ids, rejected_attention_mask = self._pad_to_length(rejected_input_ids, rejected_attention_mask)
input_ids = torch.stack((chosen_input_ids, rejected_input_ids), dim=0)
attention_mask = torch.stack((chosen_attention_mask, rejected_attention_mask), dim=0)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
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