Feature Request Multi-GPU Splitting For Insufficient Single-GPU Memory In Wan-Video

by StackCamp Team 84 views

Hey guys! 👋 Let's dive into this feature request for Wan-Video, specifically addressing the challenge of running tasks on machines with multiple GPUs where single-GPU memory might be a bottleneck. This is a crucial topic, especially for those of us working with high-resolution video generation.

The Challenge: Insufficient Single-GPU Memory

When working with models like Wan-Video, especially for tasks such as image-to-video generation (i2v), the memory requirements can be substantial. Imagine you're rocking a setup with multiple GPUs, say 8 x 4090s with 24GB each, which is pretty powerful! But, when you try running a command like this:

torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

You might still run into an Out-of-Memory (OOM) error. Why? Because even though you have plenty of aggregate memory (8 GPUs x 24GB = 192GB), a single GPU might not have enough memory to load the entire model or handle the intermediate computations for a large video generation task. This is a common pain point when dealing with large models and high-resolution outputs.

Keywords: insufficient single-GPU memory, Out-of-Memory (OOM) error, multi-GPU, Wan-Video, high-resolution video generation

Understanding the Memory Bottleneck

Before we get into potential solutions, let's quickly break down why this happens. Models like Wan-Video often have a massive number of parameters. These parameters, along with the intermediate activations generated during the forward and backward passes, need to be stored in GPU memory. When you're generating high-resolution videos, the intermediate tensors can become extremely large, quickly exceeding the capacity of a single GPU, even with 24GB of VRAM.

Think of it like this: you have a team of eight super-strong people (your GPUs), but you're asking one of them to carry an entire house (the model and computations). Even though the team as a whole could easily lift the house if they worked together, one person simply can't do it alone. That's where multi-GPU splitting comes in!

The Need for Model and Data Parallelism

To effectively utilize multi-GPU systems, we need strategies that distribute the workload across multiple devices. Two primary techniques come into play:

  • Model Parallelism: This involves splitting the model itself across multiple GPUs. Different layers or sub-modules of the model reside on different GPUs. During computation, data is passed between these GPUs to complete the forward and backward passes. This is especially useful for extremely large models that simply cannot fit on a single GPU.
  • Data Parallelism: This involves replicating the model on each GPU and splitting the input data into batches. Each GPU processes a different batch of data, and the gradients are synchronized across GPUs during training. This approach is effective for increasing throughput, as each GPU works on a different part of the data simultaneously.

Keywords: Model Parallelism, Data Parallelism, multi-GPU systems, memory requirements, large models

The Feature Request: Multi-GPU Splitting

The core of this feature request is to enable multi-GPU splitting within Wan-Video. This means implementing a mechanism to intelligently distribute the model and its computations across multiple GPUs, even if a single GPU doesn't have enough memory to handle the entire load. This is a game-changer for users with multi-GPU setups who are currently hitting OOM errors when trying to generate high-quality videos.

The original user's question boils down to a few key points:

  1. Does Wan-Video currently support multi-GPU splitting for inference? This is the most pressing question. If the feature exists, how do we use it?
  2. If multi-GPU splitting is supported, can you provide specific configuration instructions or example scripts? Practical examples are essential for users to get up and running quickly.
  3. If multi-GPU splitting is not currently supported, are there plans to add this functionality in future versions? Knowing the roadmap helps users plan their work and potential contributions.

Keywords: multi-GPU splitting, feature request, Wan-Video configuration, example scripts, future development plans

Why Multi-GPU Splitting is Crucial

Let's underscore why this feature is so important:

  • Unlocking High-Resolution Generation: Multi-GPU splitting allows users to generate videos at higher resolutions and complexities, pushing the boundaries of what's possible with Wan-Video.
  • Improved Performance: Distributing the workload across multiple GPUs can significantly reduce generation time, making the process more efficient.
  • Wider Hardware Compatibility: By alleviating the single-GPU memory bottleneck, Wan-Video can be used on a broader range of hardware configurations.
  • Scalability: As models grow larger and video resolutions increase, multi-GPU support becomes increasingly critical for scaling video generation capabilities.

Keywords: high-resolution generation, performance improvement, hardware compatibility, scalability, multi-GPU support

Current Status and Potential Solutions

So, where does Wan-Video stand with multi-GPU splitting? Let's explore the possibilities and potential implementation strategies.

Existing FSDP Implementation

The command provided by the user includes the flags --dit_fsdp and --t5_fsdp. FSDP stands for Fully Sharded Data Parallelism, which is a PyTorch feature designed for training large models across multiple GPUs. FSDP shards the model parameters across GPUs, reducing the memory footprint on each individual device. This suggests that Wan-Video already has some level of multi-GPU support, at least for training.

However, the question remains: is FSDP being utilized effectively for inference? It's possible that the current implementation is primarily geared towards training and might not be fully optimized for inference scenarios, especially with large input sizes and complex prompts. It's also possible that certain parts of the model or computation graph are not being sharded, leading to memory bottlenecks on a single GPU.

Keywords: Fully Sharded Data Parallelism (FSDP), PyTorch, multi-GPU training, inference optimization, model sharding

Potential Implementation Approaches

If FSDP isn't fully addressing the inference memory issue, here are some potential avenues for improvement:

  1. Fine-tuning FSDP for Inference: This involves optimizing the FSDP configuration specifically for inference. This might include adjusting the sharding strategy, activation checkpointing, and other parameters to minimize memory consumption during the forward pass.
  2. Model Parallelism Techniques: Exploring other model parallelism techniques beyond FSDP could be beneficial. This might involve manually partitioning the model and distributing it across GPUs, or using libraries like DeepSpeed, which offer advanced model parallelism capabilities.
  3. Pipeline Parallelism: This approach divides the computation into stages and assigns each stage to a different GPU. Data flows through the pipeline, with each GPU processing a specific part of the computation. This can be particularly effective for models with a sequential structure.
  4. Memory-Efficient Operators: Identifying and replacing memory-intensive operators with more efficient alternatives can also help reduce the memory footprint. This might involve using techniques like operator fusion or custom CUDA kernels.

Keywords: FSDP optimization, model parallelism techniques, DeepSpeed, pipeline parallelism, memory-efficient operators, CUDA kernels

Providing Configuration Instructions and Examples

A crucial part of addressing this feature request is providing clear and concise instructions on how to enable and configure multi-GPU splitting. This includes:

  • Command-line arguments: Documenting the specific flags and options that control multi-GPU behavior (e.g., --nproc_per_node, --dit_fsdp, --t5_fsdp, and any new flags introduced for inference).
  • Configuration files: Providing example configuration files that users can adapt to their specific hardware and workloads.
  • Code examples: Offering code snippets that demonstrate how to load the model, split it across GPUs, and perform inference in a multi-GPU setting.
  • Troubleshooting tips: Including common issues and solutions related to multi-GPU setup and OOM errors.

Keywords: configuration instructions, command-line arguments, configuration files, code examples, troubleshooting tips, multi-GPU setup

Example Script Scenario

Let's imagine a simplified example script to illustrate how multi-GPU splitting might work:

import torch
import wan_video

def main():
    # 1. Initialize distributed environment (if not already done)
    torch.distributed.init_process_group(backend="nccl")
    local_rank = int(os.environ["LOCAL_RANK"])
    torch.cuda.set_device(local_rank)
    device = torch.device("cuda", local_rank)

    # 2. Load the model (potentially sharded across GPUs)
    model = wan_video.load_model(..., device=device, shard_model=True) # 'shard_model' is our hypothetical flag

    # 3. Prepare input data
    input_image = load_image("examples/i2v_input.JPG").to(device)
    prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard..."

    # 4. Generate the video
    with torch.no_grad():
        video = model.generate(input_image, prompt)

    # 5. Save the video (potentially gather results from all GPUs)
    save_video(video, "output.mp4")

if __name__ == "__main__":
    main()

This is a highly simplified illustration, but it highlights the key steps involved in multi-GPU inference: initializing the distributed environment, loading the model with sharding, preparing the input data, generating the video, and saving the output. A real-world implementation would involve more intricate details, but this provides a general idea.

Future Plans and Community Contributions

Finally, understanding the roadmap for multi-GPU support in Wan-Video is crucial. If this feature is not currently prioritized, the community might be able to contribute. Open-source projects thrive on collaboration, and contributions from users facing this issue could significantly accelerate the development process.

This could involve:

  • Code contributions: Implementing multi-GPU splitting techniques, optimizing existing code, and writing unit tests.
  • Documentation: Creating tutorials, examples, and guides on using multi-GPU features.
  • Testing and feedback: Identifying bugs, providing performance benchmarks, and suggesting improvements.

Keywords: community contributions, open-source projects, code contributions, documentation, testing and feedback, future roadmap

In Conclusion

Enabling multi-GPU splitting for inference in Wan-Video is a critical step towards unlocking its full potential. By addressing the single-GPU memory bottleneck, we can enable higher-resolution generation, improve performance, and broaden hardware compatibility. Whether through optimizing FSDP, exploring other model parallelism techniques, or relying on community contributions, this feature will significantly benefit Wan-Video users.

Let's keep the conversation going! What are your thoughts on multi-GPU splitting? What techniques have you found effective in other projects? Share your ideas and experiences in the comments below! 👇