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Running
on
Zero
File size: 12,003 Bytes
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import contextlib
import json
import logging
import os
import pdb
import re
import traceback
import uuid
import numpy as np
import torch
import yaml
from PIL import Image
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from .processor.audio_processor import AudioProcessor
from .processor.image_processor import ImageProcessor
from .utils import draw_data
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
class BaseDataset(torch.utils.data.Dataset):
def __init__(
self,
cfg_path,
tokenizer,
image_size=448,
image_token_length=1024,
max_padding_length=32768,
variable_length=False,
output_dir="",
add_task_symbol=True,
training_args=None,
shift_token=False,
create_position_ids=True,
create_attention_mask=True,
create_attention_mask_2d=False,
create_loss_mask=False,
max_num_frame=8,
max_fps=1,
reset_position_ids=False,
reset_attention_mask=False,
min_patch_grid=1,
max_patch_grid=6,
process_type="anyres",
normalize_type="imagenet",
seed=42,
cross_dataset_joint=False,
dataset_joint=True,
audio_tokenizer_type=None,
audio_tokenizer_path=None,
text_audio_interval_ratio=None,
use_megatron=True,
):
super(BaseDataset, self).__init__()
self.cfg_path = cfg_path
with open(self.cfg_path, "r", encoding="utf8") as cfg_file:
cfg_data = cfg_file.read()
self.cfg = yaml.load(cfg_data, Loader=yaml.CLoader)
logger.info(f"cfg {self.cfg}")
self.tokenizer = tokenizer
self.max_padding_length = max_padding_length
self.variable_length = variable_length
self.output_dir = output_dir
self.training_args = training_args
self.shift_token = shift_token
self.create_position_ids = create_position_ids
self.create_attention_mask = create_attention_mask
self.create_attention_mask_2d = create_attention_mask_2d
self.create_loss_mask = create_loss_mask
self.max_num_frame = max_num_frame
self.max_fps = max_fps
self.reset_position_ids = reset_position_ids
self.reset_attention_mask = reset_attention_mask
self.seed = seed
self.cross_dataset_joint = cross_dataset_joint
self.dataset_joint = dataset_joint
self.image_size = image_size
self.image_token_length = image_token_length
self.do_dataset_format = self.cfg.get("do_dataset_format", False)
self.do_dataset_cast = self.cfg.get("do_dataset_cast", False)
self.xlsx_sample_num = self.cfg.get("xlsx_sample_num", 5)
self.processor = {}
self.processor["image"] = ImageProcessor(
process_type,
image_size=self.image_size,
normalize_type=normalize_type,
min_patch_grid=min_patch_grid,
max_patch_grid=max_patch_grid,
)
self.processor["audio"] = AudioProcessor(
audio_tokenizer_path=audio_tokenizer_path,
audio_tokenizer_type=audio_tokenizer_type,
text_audio_interval_ratio=text_audio_interval_ratio
)
if use_megatron:
self.load_data()
else:
with main_process_first(local=True, desc="Loading data"):
self.load_data()
self.processed_samples = 0
self.unjoint_samples = 0
self.joint_samples = 0
self.skip_samples = 0
def load_data(self):
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
raw_data = None
sampled_data = {}
source_idx = 0
for data_name, data_info in self.cfg["dataset"].items():
data_ratio = data_info.get("ratio", 1)
data_num = data_info.get("num", 999999999)
if data_ratio == 0:
continue
if data_num == 0:
continue
for data_idx, data_path in enumerate(data_info["data_paths"]):
if not os.path.isfile(data_path) and not os.path.isdir(data_path):
logger.warning(f"Data file no found {data_path}")
continue
this_data = load_json(data_path, self.output_dir)
# this_data = load_data_one(data_path, self.outout_dir)
if this_data is None:
logger.warning(f"Failed to load {data_path}")
continue
# print(f"this_data {this_data}")
column_names = list(this_data.features)
if "id" in column_names:
this_data = this_data.remove_columns("id")
# sources = [data_path] * len(this_data)
sources = [source_idx] * len(this_data)
source_idx += 1
# sources = [data_name] * len(this_data)
this_data = this_data.add_column("source", sources)
if "images" not in column_names:
# images = [[]] * len(this_data)
images = [None] * len(this_data)
this_data = this_data.add_column("images", images)
if "videos" not in column_names:
# videos = [[]] * len(this_data)
videos = [None] * len(this_data)
this_data = this_data.add_column("videos", videos)
if "audios" not in column_names:
# videos = [[]] * len(this_data)
audios = [None] * len(this_data)
this_data = this_data.add_column("audios", videos)
if False:
column_names = list(this_data.features)
this_data = this_data.map(
format_function_general,
batched=True,
batch_size=2560,
num_proc=1,
# batch_size=1,
# num_proc=1,
remove_columns=column_names,
keep_in_memory=False,
desc="Running format on dataset",
)
this_data = this_data.shuffle(seed=self.seed)
# this_data = this_data.flatten_indices()
this_data = this_data.shuffle(seed=self.seed)
# this_data = this_data.flatten_indices()
data_ratio = float(data_ratio)
total_num = len(this_data)
used_num = min(int(total_num * data_ratio), data_num)
logger.info(f"total_num {total_num}")
logger.info(f"data_ratio {data_ratio}")
logger.info(f"data_num {data_num}")
logger.info(f"used_num {used_num}")
indices = [x % total_num for x in range(used_num)]
this_data = this_data.select(indices)
if raw_data is None:
raw_data = this_data
else:
if self.do_dataset_cast:
this_data = this_data.cast(raw_data.features)
raw_data = concatenate_datasets([raw_data, this_data])
sampled_data[data_path] = {}
sampled_data[data_path]["data"] = this_data.select(
range(min(self.xlsx_sample_num, used_num))
)
sampled_data[data_path]["total_num"] = total_num
sampled_data[data_path]["used_num"] = used_num
logger.info(f"this_data {this_data}")
logger.info(f"raw_data {raw_data}")
# logger.info(f"raw_data {raw_data[0]}")
# logger.info(f"raw_data {raw_data[-1]}")
logger.info(f"Successful load {data_path}")
raw_data = raw_data.shuffle(seed=self.seed)
# raw_data = raw_data.flatten_indices()
raw_data = raw_data.shuffle(seed=self.seed)
# raw_data = raw_data.flatten_indices()
self.raw_data = raw_data
if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
output_xlsx = os.path.basename(self.cfg_path).replace("yaml", "xlsx")
output_xlsx = os.path.join(self.output_dir, output_xlsx)
logger.info(f"output_xlsx {output_xlsx}")
draw_data(
sampled_data,
output_xlsx,
tokenizer=self.tokenizer,
image_processor=self.processor["image"],
)
logger.info(f"raw_data {raw_data}")
if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
logger.info(f"raw_data {raw_data[:10]}")
logger.info(f"raw_data {raw_data[-10:]}")
def __len__(self):
return len(self.raw_data)
def format_function_general(examples):
messages = [x for x in examples["messages"]]
if "images" in examples:
images = [x for x in examples["images"]]
else:
images = [None for _ in messages]
if "videos" in examples:
videos = [x for x in examples["videos"]]
else:
videos = [None for _ in messages]
if "audios" in examples:
audios = [x for x in examples["audios"]]
else:
audios = [None for _ in messages]
return {
"messages": messages,
"images": images,
"videos": videos,
"audios": audios,
}
def load_json_A(data_file):
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
with open(data_file, "r") as f:
raw_data = json.load(f)
this_data = Dataset.from_list(raw_data)
return this_data
def load_json_B(data_file):
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
this_data = load_dataset("json", data_files=data_file, keep_in_memory=False)
return this_data["train"]
def load_json_C(data_file):
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
raw_data = []
with open(data_file, "r") as f:
for line in f.readlines():
d = json.loads(line)
# raw_data.append({"conversations": d["conversations"], "id": d["id"]})
if "conversations" in d:
raw_data.append({"conversations": d["conversations"]})
if "messages" in d:
raw_data.append({"messages": d["messages"]})
this_data = Dataset.from_list(raw_data)
return this_data
def load_json(data_file, output_dir):
for func in [load_json_B, load_json_A, load_json_C]:
try:
this_data = func(data_file)
return this_data
except Exception as error:
with open(os.path.join(output_dir, "data_error.log"), "a") as f:
print("-" * 100, file=f)
# print(error, file=f)
print(traceback.format_exc(), file=f)
continue
return None
def load_data_one(data_file, output_dir):
if data_file.endswith("json") or data_file.endswith("jsonl"):
return load_json(data_file, output_dir)
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
this_data = load_dataset(data_file, keep_in_memory=False)
return this_data["train"]
@contextlib.contextmanager
def main_process_first(local=True, desc="work"):
if torch.distributed.is_initialized() and torch.distributed.get_world_size() > 1:
if local:
rank = int(os.environ["LOCAL_RANK"])
else:
rank = torch.distributed.get_rank()
is_main_process = rank == 0
try:
if not is_main_process:
torch.distributed.barrier()
yield
finally:
if is_main_process:
torch.distributed.barrier()
else:
yield
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