inf-cl 1.2

A highly memory-efficient contrastive loss.

Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss

If our project helps you, please give us a star ⭐ on GitHub to support us. 🙏🙏

💡 Some other multimodal foundation model projects from our team may interest you ✨.

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VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
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The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio
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📰 News

• [2024.10.18] Release training, evaluation codes of Inf-CLIP.

🛠️ Requirements and Installation

Basic Dependencies:

• Python >= 3.8
• Pytorch >= 2.0.0
• CUDA Version >= 11.8

[Remote] Install Inf-CL:

# remote installing
pip install inf_cl

[Local] Install Inf-CL:

pip install -e .

Install required packages:

git clone https://github.com/DAMO-NLP-SG/Inf-CLIP
cd Inf-CLIP
pip install -r requirements.txt

⭐ Features

inf_cl is the triton implementation of Inf-CL loss:

• Ring-CL (inf_cl/ring.py#L238)
• Inf-CL (inf_cl/ring.py#L251)

inf_clip is the CLIP training codebase with Inf-CL loss and other training features:

• Gradient Accumulation (inf_clip/train/train.py#L180)
• Gradient Cache (inf_clip/train/train.py#L292)

🔑 Usage

A simple example about how to adopt our Inf-CL loss for contrastive learning. Using such command for attempting:

torchrun --nproc_per_node 2 tests/example.py

import torch
import torch.nn.functional as F
import torch.distributed as dist
import numpy as np

from inf_cl import cal_inf_loss


def create_cl_tensors(rank, world_size):
    # Parameters
    dtype = torch.float32
    num_heads = 3        # Number of attention heads
    seq_length_q = 32768 # Sequence length
    seq_length_k = 32768
    d_model = 256        # Dimension of each head (must be 16, 32, 64, or 128)

    # Randomly initialize inputs
    q = torch.rand((seq_length_q // world_size, num_heads * d_model), dtype=dtype, device=f"cuda:{rank}")
    k = torch.rand((seq_length_k // world_size, num_heads * d_model), dtype=dtype, device=f"cuda:{rank}")
    l = torch.ones([], dtype=dtype, device=f"cuda:{rank}") * np.log(1 / 0.07)

    q = F.normalize(q, p=2, dim=-1).requires_grad_() # Query
    k = F.normalize(k, p=2, dim=-1).requires_grad_() # Key
    l = l.requires_grad_() # Logit scale

    return q, k, l


if __name__ == "__main__":
    # Assume that the distributed environment has been initialized
    dist.init_process_group("nccl")

    rank = dist.get_rank()
    world_size = dist.get_world_size()

    torch.cuda.set_device(rank)

    # Exampled by Image-Text Contrastive Learning, q is the global image features, 
    # k is the text features, and l is the logit scale.
    q, k, l = create_cl_tensors(rank, world_size)

    # labels are diagonal elements by default. 
    # labels = torch.arange(q.shape[0])
    loss = cal_inf_loss(q, k, scale=l.exp())

    print(loss)

🚀 Main Results

Memory Cost

* denotes adopting "data offload" strategy.

Max Supported Batch Size

Speed

Batch Size Scaling

Training with larger data scale needs larger batch size.

🗝️ Training & Evaluation

Quick Start

To facilitate further development on top of our codebase, we provide a quick-start guide on how to use Inf-CLIP to train a customized CLIP and evaluate the trained model on the mainstream clip benchmarks.

1. Training Data Structure:

Inf-CLIP
├── datasets
│   ├── cc3m/ # https://github.com/rom1504/img2dataset/blob/main/dataset_examples/cc3m.md
|   |   ├── 0000.tar
|   |   ├── 0001.tar
|   |   ├── ...
|   |   └── 0301.tar
│   ├── cc12m/ # https://github.com/rom1504/img2dataset/blob/main/dataset_examples/cc12m.md
|   |   ├── 0000.tar
|   |   ├── 0001.tar
|   |   ├── ...
|   |   └── 1044.tar
│   ├── laion400m/ # https://github.com/rom1504/img2dataset/blob/main/dataset_examples/laion400m.md
|   |   ├── 00000.tar
|   |   ├── 00001.tar
|   |   ├── ...
|   |   └── 41407.tar

2. Command:

bash scripts/cc3m/lit_vit-b-32_bs16k.sh
bash scripts/cc12m/lit_vit-b-32_bs32k.sh
bash scripts/laion400m/lit_vit-b-32_bs256k.sh

3. Evaluation Data Structure:

Inf-CLIP
├── datasets
│   ├── imagenet-1k/ # download val_images.tar.gz of imagenet
|   |   └── val/
|   |   |   ├── n01440764
|   |   |   ├── n01443537
|   |   |   ├── ...
|   |   |   └── n15075141
│   ├── clip-benchmark/ # bash datasets/benchmarks_download.sh
|   |   ├── wds_mscoco_captions
|   |   ├── wds_flickr8k
|   |   ├── wds_flickr30k
|   |   ├── wds_imagenet1k
|   |   ├── wds_imagenetv2
|   |   ├── wds_imagenet_sketch
|   |   ├── wds_imagenet-a
|   |   ├── wds_imagenet-r
|   |   ├── wds_imagenet-o
|   |   └── wds_objectnet

4. Command:

# imagenet evaluation
bash scripts/imagenet_eval.sh
# overall evaluation
bash scripts/benchmarks_eval.sh

📑 Citation

If you find Inf-CLIP useful for your research and applications, please cite using this BibTeX:

@article{damonlpsg2024infcl,
  title={Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss},
  author={Zesen Cheng, Hang Zhang, Kehan Li, Sicong Leng, Zhiqiang Hu, Fei Wu, Deli Zhao, Xin Li, Lidong Bing},
  journal={arXiv preprint arXiv:},
  year={2024},
  url = {https://arxiv.org/abs/}
}

👍 Acknowledgement

The codebase of Inf-CLIP is adapted from OpenCLIP. We are also grateful for the following projects our Inf-CL arise from:

• OpenAI CLIP, img2dataset, CLIP-Benchmark.
• FlashAttention, RingAttention, RingFlashAttention.

🔒 License

This project is released under the Apache 2.0 license as found in the LICENSE file. The service is a research preview intended for non-commercial use ONLY, subject to the model Licenses of CLIP, Terms of Use of the data generated by OpenAI, and Laion. Please get in touch with us if you find any potential violations.