Multimodal task resource optimization
The following memory management strategies can be implemented when processing multimodal tasks such as image + text:
- Chunking technology: Using ImageProcessor's chunking parameter
from transformers import AutoImageProcessor
processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
processor.feature_extractor.size = {"height":256, "width":256} - gradient checkpoint: Activating PyTorch's checkpoint mechanism
model.gradient_checkpointing_enable()
- Mixed precision training: fp16 optimizer with DeepSpeed
"fp16": {"enabled": "auto"}
Case in point: When using ColQwen2 to process A4 documents, setting the chunk size to 512px reduces the video memory requirement from 24GB to 8GB.
This answer comes from the articleTransformers: open source machine learning modeling framework with support for text, image and multimodal tasksThe
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