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Distributed inference with multiple GPUs

On distributed setups, you can run inference across multiple GPUs with 🤗 Accelerate or PyTorch Distributed, which is useful for generating with multiple prompts in parallel.

This guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference.

##
🤗 Accelerate

🤗 Accelerate is a library designed to make it easy to train or run inference across distributed setups. It simplifies the process of setting up the distributed environment, allowing you to focus on your PyTorch code.

To begin, create a Python file and initialize an accelerate.PartialState to create a distributed environment; your setup is automatically detected so you don’t need to explicitly define the `rank` or `world_size`. Move the DiffusionPipeline to `distributed_state.device` to assign a GPU to each process.

Now use the split_between_processes utility as a context manager to automatically distribute the prompts between the number of processes.

    import torch
    from accelerate import PartialState
    from diffusers import DiffusionPipeline

    pipeline = DiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
    )
    distributed_state = PartialState()
    pipeline.to(distributed_state.device)

    with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
        result = pipeline(prompt).images[0]
        result.save(f"result_{distributed_state.process_index}.png")

Use the `--num_processes` argument to specify the number of GPUs to use, and call `accelerate launch` to run the script:

    accelerate launch run_distributed.py --num_processes=2

Refer to this minimal example script for running inference across multiple GPUs. To learn more, take a look at the Distributed Inference with 🤗 Accelerate guide.

##
PyTorch Distributed

PyTorch supports `DistributedDataParallel` which enables data parallelism.

To start, create a Python file and import `torch.distributed` and `torch.multiprocessing` to set up the distributed process group and to spawn the processes for inference on each GPU. You should also initialize a DiffusionPipeline:

    import torch
    import torch.distributed as dist
    import torch.multiprocessing as mp

    from diffusers import DiffusionPipeline

    sd = DiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
    )

You’ll want to create a function to run inference; `init_process_group` handles creating a distributed environment with the type of backend to use, the `rank` of the current process, and the `world_size` or the number of processes participating. If you’re running inference in parallel over 2 GPUs, then the `world_size` is 2.

Move the DiffusionPipeline to `rank` and use `get_rank` to assign a GPU to each process, where each process handles a different prompt:

    def run_inference(rank, world_size):
        dist.init_process_group("nccl", rank=rank, world_size=world_size)

        sd.to(rank)

        if torch.distributed.get_rank() == 0:
            prompt = "a dog"
        elif torch.distributed.get_rank() == 1:
            prompt = "a cat"

        image = sd(prompt).images[0]
        image.save(f"./{'_'.join(prompt)}.png")

To run the distributed inference, call `mp.spawn` to run the `run_inference` function on the number of GPUs defined in `world_size`:

    def main():
        world_size = 2
        mp.spawn(run_inference, args=(world_size,), nprocs=world_size, join=True)

    if __name__ == "__main__":
        main()

Once you’ve completed the inference script, use the `--nproc_per_node` argument to specify the number of GPUs to use and call `torchrun` to run the script:

    torchrun run_distributed.py --nproc_per_node=2

You can use `device_map` within a DiffusionPipeline to distribute its model-level components on multiple devices. Refer to the Device placement guide to learn more.

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