File size: 8,176 Bytes
857c2e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | import json
import os
import numpy as np
from PIL import Image
from copy import deepcopy
from transformers import AutoTokenizer, Qwen2VLImageProcessor
from torchcodec.decoders import VideoDecoder
import binpacking
from tqdm import tqdm
import concurrent.futures
import time
def read_data(file_path):
"""Read JSON or JSONL file"""
if file_path.endswith(('.json', '.jsonl')):
with open(file_path, 'r') as f:
if file_path.endswith('.json'):
return json.load(f)
return [json.loads(line) for line in f]
raise ValueError('Please provide a .json or .jsonl file')
def write_data(file_path, data):
"""Write data to JSON or JSONL file"""
with open(file_path, 'w') as f:
if file_path.endswith('.json'):
json.dump(data, f, indent=4)
elif file_path.endswith('.jsonl'):
for item in data:
f.write(json.dumps(item) + '\n')
class DataArguments:
def __init__(self):
self.max_pixels = 2048 * 28 * 28
self.min_pixels = 32 * 28 * 28
self.video_max_frame_pixels = 576 * 28 * 28
self.video_min_frame_pixels = 144 * 28 * 28
self.base_interval = 4
self.video_min_frames = 4
self.video_max_frames = 8
self.data_path = ''
class MultimodalProcessor:
def __init__(self, data_args, base_processor, device='cpu'):
self.data_args = data_args
self.base_processor = base_processor
self.device = device
def _configure_processor(self, max_val, min_val):
processor = deepcopy(self.base_processor)
processor.max_pixels = max_val
processor.min_pixels = min_val
processor.size = {'longest_edge': max_val, 'shortest_edge': min_val}
return processor
def process_image(self, image_file):
image_path = os.path.join(self.data_args.data_path, image_file)
if not os.path.exists(image_path):
print(f'Image file does not exist: {image_path}')
return 0
processor = self._configure_processor(self.data_args.max_pixels, self.data_args.min_pixels)
image = Image.open(image_path).convert('RGB')
visual_processed = processor.preprocess(images=image, return_tensors='pt')
return visual_processed['image_grid_thw'].prod() // 4
def process_video(self, video_file):
video_path = os.path.join(self.data_args.data_path, video_file)
processor = self._configure_processor(self.data_args.video_max_frame_pixels, self.data_args.video_min_frame_pixels)
decoder = VideoDecoder(video_path, device=self.device)
total_frames = decoder.metadata.num_frames
avg_fps = decoder.metadata.average_fps
video_length = total_frames / avg_fps
interval = self.data_args.base_interval
num_frames_to_sample = round(video_length / interval)
target_frames = min(max(num_frames_to_sample, self.data_args.video_min_frames), self.data_args.video_max_frames)
frame_idx = np.unique(np.linspace(0, total_frames - 1, target_frames, dtype=int)).tolist()
frame_batch = decoder.get_frames_at(indices=frame_idx)
video_frames_numpy = frame_batch.data.cpu().numpy()
visual_processed = processor.preprocess(images=None, videos=video_frames_numpy, return_tensors='pt')
return visual_processed['video_grid_thw'].prod() // 4
def calculate_tokens(conversation, processor, tokenizer):
total_tokens = 21
roles = {'human': 'user', 'gpt': 'assistant'}
for message in conversation['conversations']:
role = message['from']
text = message['value']
conv = [{'role': roles[role], 'content': text}]
encode_id = tokenizer.apply_chat_template(conv, return_tensors='pt', add_generation_prompt=False)[0]
total_tokens += len(encode_id)
if 'image' in conversation:
images = conversation['image'] if isinstance(conversation['image'], list) else [conversation['image']]
for image_file in images:
total_tokens += processor.process_image(image_file)
elif 'video' in conversation:
videos = conversation['video'] if isinstance(conversation['video'], list) else [conversation['video']]
for video_file in videos:
total_tokens += processor.process_video(video_file)
return total_tokens
def pack_data(data_list, pack_length):
# Extract the length of each data item
lengths = [data["num_tokens"] for data in data_list]
grouped_indices = binpacking.to_constant_volume(
list(enumerate(lengths)), # Explicitly convert to list
pack_length,
weight_pos=1
)
packed_data = []
for group in grouped_indices:
group_data = []
for index, _ in group:
new_data = data_list[index].copy()
new_data.pop("num_tokens", None)
group_data.append(new_data)
packed_data.append(group_data)
return packed_data
datasets = {
'dummy_dataset': {
'data_path': '',
'annotation_path': 'path/to/your/annotation.json'
}
}
data_args = DataArguments()
model_path = 'path/to/your/model'
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
base_image_processor = Qwen2VLImageProcessor.from_pretrained(model_path)
print(f'Successfully loaded model components from {model_path}')
processor = MultimodalProcessor(data_args, base_image_processor, device='cpu')
for dataset_name, config in datasets.items():
processor.data_args.data_path = config['data_path']
annotation_path = os.path.join(processor.data_args.data_path, config['annotation_path'])
print(f'\n--- Processing dataset: {dataset_name} ---')
print(f'Annotation file path: {annotation_path}')
print(f'Image configuration: max_pixels={data_args.max_pixels}, min_pixels={data_args.min_pixels}')
print(f'Video frame configuration: video_max_frame_pixels={data_args.video_max_frame_pixels}, video_min_frame_pixels={data_args.video_min_frame_pixels}')
if not os.path.exists(annotation_path):
print(f'Annotation file not found: {annotation_path}')
continue
data = read_data(annotation_path)
count_file_path = annotation_path.replace('.jsonl', '_count.json').replace('.json', '_count.json')
if os.path.exists(count_file_path):
print(f"Found pre - calculated token counts, loading data from {count_file_path}.")
data_with_tokens = read_data(count_file_path)
else:
def calculate_and_update(item):
item['num_tokens'] = calculate_tokens(item, processor, tokenizer)
return item
with concurrent.futures.ThreadPoolExecutor() as executor:
data_with_tokens = list(tqdm(executor.map(calculate_and_update, data), total=len(data), desc=f"Processing {dataset_name} data"))
# Save the token count results
write_data(count_file_path, data_with_tokens)
print(f"Token counts saved to: {count_file_path}")
# Assume the packing length is 4096
pack_length = 4096
# Define the batch size
batch_size = 256
all_packed_results = []
# Record the start time of binpacking
start_time = time.time()
for i in range(0, len(data_with_tokens), batch_size):
batch_data = data_with_tokens[i: i + batch_size]
batch_packed_result = pack_data(batch_data, pack_length)
all_packed_results.extend(batch_packed_result)
# Record the end time of binpacking
end_time = time.time()
# Calculate the time spent on binpacking
binpack_time = end_time - start_time
print(f"Time spent on binpacking: {binpack_time:.4f} seconds")
# Save the packed results as a JSON file
pack_output_path = annotation_path.replace('.jsonl', '_pack.json').replace('.json', '_pack.json')
with open(pack_output_path, 'w', encoding='utf-8') as file:
json.dump(all_packed_results, file, indent=2)
print(f"Packed results saved to: {pack_output_path}") |