143 lines
5.4 KiB
Python
143 lines
5.4 KiB
Python
# 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 transformers import AutoTokenizer
|
|
|
|
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((rejected_input_ids, rejected_attention_mask), dim=0)
|
|
|
|
return {
|
|
'input_ids': input_ids,
|
|
'attention_mask': attention_mask,
|
|
} |