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#
# 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 math
import os
from collections import defaultdict
from io import BytesIO
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from datasets import load_dataset
from jinja2 import Template
from PIL import Image
from PIL.Image import Image as ImageObject
from qwen_vl_utils.vision_process import fetch_video
from torch.utils.data import Dataset
from transformers import PreTrainedTokenizer, ProcessorMixin
from ..models.transformers.qwen2_vl import get_rope_index
from . import torch_functional as VF
def collate_fn(features: List[Dict[str, Any]]) -> Dict[str, Any]:
tensors = defaultdict(list)
non_tensors = defaultdict(list)
for feature in features:
for key, value in feature.items():
if isinstance(value, torch.Tensor):
tensors[key].append(value)
else:
non_tensors[key].append(value)
for key, value in tensors.items():
tensors[key] = torch.stack(value, dim=0)
for key, value in non_tensors.items():
non_tensors[key] = np.array(value, dtype=object)
return {**tensors, **non_tensors}
def process_image(
image: Union[Dict[str, Any], ImageObject, str], min_pixels: Optional[int], max_pixels: Optional[int]
) -> ImageObject:
if isinstance(image, str):
image = Image.open(image)
elif isinstance(image, dict):
image = Image.open(BytesIO(image["bytes"]))
elif isinstance(image, bytes):
image = Image.open(BytesIO(image))
image.load() # avoid "Too many open files" errors
if max_pixels is not None and (image.width * image.height) > max_pixels:
resize_factor = math.sqrt(max_pixels / (image.width * image.height))
width, height = int(image.width * resize_factor), int(image.height * resize_factor)
image = image.resize((width, height))
if min_pixels is not None and (image.width * image.height) < min_pixels:
resize_factor = math.sqrt(min_pixels / (image.width * image.height))
width, height = int(image.width * resize_factor), int(image.height * resize_factor)
image = image.resize((width, height))
if image.mode != "RGB":
image = image.convert("RGB")
return image
def process_video(
video: str, min_pixels: Optional[int], max_pixels: Optional[int], video_fps: float, return_fps: bool = False
) -> Union[List[ImageObject], Tuple[List[ImageObject], List[float]]]:
vision_info = {"video": video, "min_pixels": min_pixels, "max_pixels": max_pixels, "fps": video_fps}
return fetch_video(vision_info, return_video_sample_fps=return_fps)
class RLHFDataset(Dataset):
"""
We assume the dataset contains a column that contains prompts and other information
"""
def __init__(
self,
data_path: str,
tokenizer: PreTrainedTokenizer,
processor: Optional[ProcessorMixin],
prompt_key: str = "prompt",
answer_key: str = "answer",
image_key: str = "images",
video_key: str = "videos",
image_dir: Optional[str] = None,
video_fps: float = 2.0,
max_prompt_length: int = 1024,
truncation: str = "error",
format_prompt: Optional[str] = None,
min_pixels: Optional[int] = None,
max_pixels: Optional[int] = None,
filter_overlong_prompts: bool = True,
filter_overlong_prompts_workers: int = 16,
):
self.tokenizer = tokenizer
self.processor = processor
self.prompt_key = prompt_key
self.answer_key = answer_key
self.image_key = image_key
self.video_key = video_key
self.image_dir = image_dir
self.video_fps = video_fps
self.max_prompt_length = max_prompt_length
self.truncation = truncation
self.min_pixels = min_pixels
self.max_pixels = max_pixels
if "@" in data_path:
data_path, data_split = data_path.split("@")
else:
data_split = "train"
if os.path.isdir(data_path):
# Check if it's a HuggingFace dataset directory (contains train/, validation/, etc.)
subdirs = [d for d in os.listdir(data_path) if os.path.isdir(os.path.join(data_path, d))]
if any(split_name in subdirs for split_name in ['train', 'validation', 'test']):
# This is a HuggingFace dataset directory, load it directly
from datasets import load_from_disk
full_dataset = load_from_disk(data_path)
self.dataset = full_dataset[data_split]
else:
# when we use dataset builder, we should always refer to the train split
file_type = os.path.splitext(os.listdir(data_path)[0])[-1][1:].replace("jsonl", "json")
self.dataset = load_dataset(file_type, data_dir=data_path, split=data_split)
elif os.path.isfile(data_path):
file_type = os.path.splitext(data_path)[-1][1:].replace("jsonl", "json")
self.dataset = load_dataset(file_type, data_files=data_path, split=data_split)
else:
# load remote dataset from huggingface hub
self.dataset = load_dataset(data_path, split=data_split)
self.format_prompt = None
if format_prompt:
with open(format_prompt, encoding="utf-8") as f:
self.format_prompt = f.read()
if filter_overlong_prompts:
self.dataset = self.dataset.filter(
self._filter_overlong_prompts,
desc="Filtering overlong prompts",
num_proc=filter_overlong_prompts_workers,
)
def _build_messages(self, example: Dict[str, Any]) -> List[Dict[str, Any]]:
prompt_str: str = example[self.prompt_key]
if self.format_prompt:
format_prompt = Template(self.format_prompt.strip())
prompt_str = format_prompt.render(content=prompt_str)
if self.image_key in example:
# https://huggingface.co/docs/transformers/en/tasks/image_text_to_text
content_list = []
for i, content in enumerate(prompt_str.split("<image>")):
if i != 0:
content_list.append({"type": "image"})
if content:
content_list.append({"type": "text", "text": content})
return [{"role": "user", "content": content_list}]
elif self.video_key in example:
content_list = []
for i, content in enumerate(prompt_str.split("<video>")):
if i != 0:
content_list.append({"type": "video"})
if content:
content_list.append({"type": "text", "text": content})
return [{"role": "user", "content": content_list}]
else:
return [{"role": "user", "content": prompt_str}]
def _filter_overlong_prompts(self, example: Dict[str, Any]) -> bool:
messages = self._build_messages(example)
if self.image_key in example:
prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
images = example[self.image_key]
if self.image_dir is not None and len(images) != 0 and isinstance(images[0], str): # image paths
images = [os.path.join(self.image_dir, image) for image in images]
processed_images = [] if len(images) != 0 else None # text-only data
for image in images:
processed_images.append(process_image(image, self.min_pixels, self.max_pixels))
model_inputs = self.processor(processed_images, [prompt], add_special_tokens=False, return_tensors="pt")
return model_inputs["input_ids"].size(-1) <= self.max_prompt_length
elif self.video_key in example:
prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
videos = example[self.video_key]
if self.image_dir is not None and len(videos) != 0 and isinstance(videos[0], str): # video paths
videos = [os.path.join(self.image_dir, video) for video in videos]
processed_videos = [] if len(videos) != 0 else None # text-only data
for video in videos:
processed_videos.append(process_video(video, self.min_pixels, self.max_pixels, self.video_fps))
model_inputs = self.processor(
videos=processed_videos, text=[prompt], add_special_tokens=False, return_tensors="pt"
)
return model_inputs["input_ids"].size(-1) <= self.max_prompt_length
else:
input_ids = self.tokenizer.apply_chat_template(messages, add_generation_prompt=True)
return len(input_ids) <= self.max_prompt_length
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
example: dict = self.dataset[index]
messages = self._build_messages(example)
example.pop(self.prompt_key, None)
if self.image_key in example:
prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
images = example.pop(self.image_key)
if self.image_dir is not None and len(images) != 0 and isinstance(images[0], str): # image paths
images = [os.path.join(self.image_dir, image) for image in images]
processed_images = [] if len(images) != 0 else None # text-only data
for image in images:
processed_images.append(process_image(image, self.min_pixels, self.max_pixels))
model_inputs = self.processor(processed_images, [prompt], add_special_tokens=False, return_tensors="pt")
input_ids = model_inputs.pop("input_ids")[0]
attention_mask = model_inputs.pop("attention_mask")[0]
example["multi_modal_data"] = {"images": images}
elif self.video_key in example:
prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
videos = example.pop(self.video_key)
if self.image_dir is not None and len(videos) != 0 and isinstance(videos[0], str): # video paths
videos = [os.path.join(self.image_dir, video) for video in videos]
processed_videos = [] if len(videos) != 0 else None # text-only data
video_fps_list = []
for video in videos:
processed_video, video_fps = process_video(
video, self.min_pixels, self.max_pixels, self.video_fps, return_fps=True
)
processed_videos.append(processed_video)
video_fps_list.append(video_fps)
model_inputs = self.processor(
videos=processed_videos, text=[prompt], add_special_tokens=False, return_tensors="pt"
)
if "second_per_grid_ts" in self.processor.model_input_names:
model_inputs["second_per_grid_ts"] = [2.0 / video_sample_fps for video_sample_fps in video_fps_list]
input_ids = model_inputs.pop("input_ids")[0]
attention_mask = model_inputs.pop("attention_mask")[0]
example["multi_modal_data"] = {"videos": videos}
else:
prompt = self.tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
model_inputs = self.tokenizer([prompt], add_special_tokens=False, return_tensors="pt")
input_ids = model_inputs.pop("input_ids")[0]
attention_mask = model_inputs.pop("attention_mask")[0]
if self.processor is not None and "Qwen2VLImageProcessor" in self.processor.image_processor.__class__.__name__:
# qwen2vl mrope
position_ids = get_rope_index(
self.processor,
input_ids=input_ids,
image_grid_thw=model_inputs.get("image_grid_thw", None),
video_grid_thw=model_inputs.get("video_grid_thw", None),
second_per_grid_ts=model_inputs.get("second_per_grid_ts", None),
attention_mask=attention_mask,
) # (3, seq_length)
else:
position_ids = torch.clip(attention_mask.cumsum(dim=0) - 1, min=0, max=None) # (seq_length,)
input_ids, attention_mask, position_ids = VF.postprocess_data(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
max_length=self.max_prompt_length,
pad_token_id=self.tokenizer.pad_token_id,
left_pad=True,
truncation=self.truncation,
)
raw_prompt_ids = self.tokenizer.encode(prompt, add_special_tokens=False)
if len(raw_prompt_ids) > self.max_prompt_length:
if self.truncation == "left":
raw_prompt_ids = raw_prompt_ids[-self.max_prompt_length :]
elif self.truncation == "right":
raw_prompt_ids = raw_prompt_ids[: self.max_prompt_length]
elif self.truncation == "error":
raise RuntimeError(f"Prompt length {len(raw_prompt_ids)} is longer than {self.max_prompt_length}.")
example["input_ids"] = input_ids
example["attention_mask"] = attention_mask
example["position_ids"] = position_ids
example["raw_prompt_ids"] = raw_prompt_ids
example["ground_truth"] = example.pop(self.answer_key)
return example
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