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datasets.py
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datasets.py
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import io
import gc
import os
import json
import random
import numpy
import torch
import base64
import pandas as pd
import os.path as op
import torch.utils.data as torch_data
from PIL import Image
from typing import List, Iterator
from muffin.data.tsv_file import TSVFile
from torch.utils.data.sampler import Sampler
from muffin.data.data_processors import register_data_processor
from muffin.eval.muffin_inference_logp import inference_logp
import datasets as hf_datasets
def bytes_to_PIL_image(img_buffer):
img_io = io.BytesIO(img_buffer)
img_io.seek(0)
image = Image.open(img_io).convert('RGB')
return image
class RLAIFVDataset(torch_data.Dataset):
def __init__(self, data_dir: str, reference_model=None,
tokenizer=None, image_token_len=None, img_processor=None, use_im_start_end=True, is_llava15=False):
super().__init__()
if not op.exists(data_dir):
os.makedirs(data_dir, exist_ok=True)
data_path = [file for file in os.listdir(data_dir) if file.endswith('.parquet') and 'logp' in file]
self.data_path = data_dir
if len(data_path) == 0:
assert reference_model is not None, "`reference_model` is mandatory when logps do not exist."
if not op.exists('./RLAIF-V-Dataset'):
os.mkdir('./RLAIF-V-Dataset')
hf_data = hf_datasets.load_dataset('openbmb/RLAIF-V-Dataset', cache_dir='./RLAIF-V-Dataset')['train'].cast_column("image", hf_datasets.Image(decode=False))
inference_logp(reference_model, tokenizer, hf_data, self.data_path,
image_token_len, img_processor, use_im_start_end, is_llava15=is_llava15)
torch.distributed.barrier()
self.data = hf_datasets.load_dataset(data_dir)['train'].cast_column("image", hf_datasets.Image(decode=False))
else:
self.data = hf_datasets.load_dataset(data_dir)['train'].cast_column("image", hf_datasets.Image(decode=False))
self.line_idx = list(range(len(self.data)))
random.shuffle(self.line_idx)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
sample = self.data[self.line_idx[index]]
question = {'from': 'human', 'value': f"<image>\n{sample['question']}"}
chosen = {'from': 'gpt', 'value': sample['chosen']}
rejected = {'from': 'gpt', 'value': sample['rejected']}
image = bytes_to_PIL_image(sample['image']['bytes'])
metainfo = {
"origin_dataset": sample['origin_dataset'],
"origin_split": sample['origin_split'],
"origin_idx": sample['idx'],
"image_id": sample['image_path'],
}
data_dict = {
'image': image,
"question": question,
"chosen": chosen,
"rejected": rejected,
"idx": sample['idx'],
"metainfo": metainfo
}
logps=json.loads(sample['logps'])
if type(logps) == type([]):
(data_dict['ref_win_logp'], data_dict['ref_win_avg_logp'], data_dict['ref_win_per_token_logp'],
data_dict['ref_rej_logp'], data_dict['ref_rej_avg_logp'], data_dict['ref_rej_per_token_logp']) = logps
else:
(data_dict['ref_win_logp'], data_dict['ref_win_avg_logp'], data_dict['ref_win_per_token_logp'],
data_dict['ref_rej_logp'], data_dict['ref_rej_avg_logp'], data_dict['ref_rej_per_token_logp']) = logps['logps']
return data_dict
class ChunckedRandomSampler(Sampler[int]):
def __init__(self, data_source, chunk_size=5000) -> None:
self.data_source = data_source
self.chunk_size = chunk_size
def __iter__(self):
n = len(self.data_source)
seed = int(torch.empty((), dtype=torch.int64).random_().item())
print(f'Chuncked Random Sampler seed is {seed}')
generator = torch.Generator()
generator.manual_seed(seed)
for st in torch.randperm(n // self.chunk_size, generator=generator).tolist():
base = st * self.chunk_size
for i in torch.randperm(self.chunk_size, generator=generator).tolist():
yield base + i
base = (n // self.chunk_size) * self.chunk_size
for i in torch.randperm(n % self.chunk_size, generator=generator).tolist():
yield base + i
def __len__(self) -> int:
return len(self.data_source)
class SingleDataSourceDataset(torch_data.Dataset):
def __init__(self, ds_name, data_dir, tsv_filenames: List[str], intent='sft', shuffle=False) -> None:
super().__init__()
self.data_dir = data_dir
self.filenames = tsv_filenames
self.ds_name = ds_name
self.sizes = []
for filename in self.filenames:
try:
size = int(filename[:-4].split('-')[-1])
except:
raise ValueError(
f'TSV Data File {filename} is not valid, last component separated by `-` must be the number of sample in this file')
self.sizes.append(size)
self.file_border_index = []
self.prepare_border_index()
self.files = self.filenames[:]
self.intent = intent
self.fetch_count = 0
self.clear_at_n_fetch = 1000 + random.randint(100, 1000)
self.shuffle = shuffle
self.line_numbers = list(range(len(self)))
if self.shuffle:
print(f'Shuffle single dataset {ds_name}', flush=True)
if len(self.line_numbers) >= 50_000_000:
self.line_numbers = list(ChunckedRandomSampler(self))
else:
random.shuffle(self.line_numbers)
def prepare_border_index(self):
self.file_border_index = [0]
temp_sum = 0
for size in self.sizes:
temp_sum += size
self.file_border_index.append(temp_sum)
def get_file_idx_and_row_idx(self, index):
found = False
file_idx = -1
for border_idx, border in enumerate(self.file_border_index):
if index < border:
file_idx = border_idx - 1
found = True
break
if not found:
raise ValueError(
f'Index {index} out of range for {self.size_sum} border markers')
offset = self.file_border_index[file_idx]
row_idx = index - offset
return file_idx, row_idx
def __len__(self):
return self.file_border_index[-1]
def __getitem__(self, index, error_count=0):
index = self.line_numbers[index]
self.fetch_count += 1
if self.fetch_count >= self.clear_at_n_fetch:
self.fetch_count = 0
# only apply to super large datasets in fact
if len(self.filenames) >= 50:
self.files = self.filenames[:]
gc.collect()
file_idx, row_idx = self.get_file_idx_and_row_idx(index)
try:
data_record = self.fetch_sample(file_idx, row_idx)
return data_record
except Exception as e:
print(
f'Encounter error while reading line-{row_idx} from {self.filenames[file_idx]}')
print(e, flush=True)
if error_count >= 3:
raise e
else:
return self.__getitem__(index + 1, error_count + 1)
def fetch_sample(self, file_idx, row_idx):
file = self.files[file_idx]
if isinstance(file, str):
self.prepare_file(file_idx)
file = self.files[file_idx]
assert isinstance(
file, TSVFile), f'Expecting TSVFile but get {file} as {type(file)}'
# tsv line as tuple
sample = file[row_idx]
ds_name, *values = sample
# data dict
sample = register_data_processor[self.ds_name](
*values, intent=self.intent)
if row_idx + 1 == len(file):
del file
# TODO: might have to update to clean memory usage?
self.files[file_idx] = self.filenames[file_idx]
return sample
def prepare_file(self, idx):
filename = self.filenames[idx]
file = TSVFile(op.join(self.data_dir, filename))
self.files[idx] = file
class MultiDataSourceDataset(torch_data.Dataset):
def __init__(self, data_sources: List[SingleDataSourceDataset], data_source_weights: List[int], shuffle=False):
super().__init__()
self.ds_list = data_sources
self.sum_weight = sum(data_source_weights)
self.ds_weights = data_source_weights
for weight in self.ds_weights:
assert isinstance(weight, int), 'weight must be integer'
self.offset2ds = {}
self.offset2wt = {}
self.offset2pd = {}
self.prepare_offset2ds()
ds_loops = []
for ds, wt in zip(self.ds_list, self.ds_weights):
ds_loop = len(ds) // wt
ds_loops.append(ds_loop)
max_loop = max(ds_loops)
self.size = max_loop * self.sum_weight
if shuffle:
for ds in self.ds_list:
assert ds.shuffle, f'Single dataset {ds} not shuffled, but multi-source dataset required to be shuffled'
def prepare_offset2ds(self):
offset = 0
for ds, weight in zip(self.ds_list, self.ds_weights):
pd = offset
for _ in range(weight):
self.offset2ds[offset] = ds
self.offset2wt[offset] = weight
self.offset2pd[offset] = pd
offset += 1
def __getitem__(self, index):
n_loop = index // self.sum_weight
offset = index % self.sum_weight
ds = self.offset2ds[offset]
ds_inner_idx = n_loop * \
self.offset2wt[offset] + offset - self.offset2pd[offset]
ds_inner_idx = ds_inner_idx % len(ds)
return ds[ds_inner_idx]
def __len__(self):
return self.size