#
Accelerated PyTorch 2.0 support in Diffusers

Starting from version `0.13.0`, Diffusers supports the latest optimization from the upcoming PyTorch 2.0 release. These include:

1. Support for accelerated transformers implementation with memory-efficient attention – no extra dependencies required.
2. torch.compile support for extra performance boost when individual models are compiled.

##
Installation

To benefit from the accelerated transformers implementation and `torch.compile`, we will need to install the nightly version of PyTorch, as the stable version is yet to be released. The first step is to install CUDA 11.7 or CUDA 11.8, as PyTorch 2.0 does not support the previous versions. Once CUDA is installed, torch nightly can be installed using:

    pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu117

##
Using accelerated transformers and torch.compile.

1. Accelerated Transformers implementation

   PyTorch 2.0 includes an optimized and memory-efficient attention implementation through the `torch.nn.functional.scaled_dot_product_attention` function, which automatically enables several optimizations depending on the inputs and the GPU type. This is similar to the `memory_efficient_attention` from xFormers, but built natively into PyTorch.

   These optimizations will be enabled by default in Diffusers if PyTorch 2.0 is installed and if `torch.nn.functional.scaled_dot_product_attention` is available. To use it, just install `torch 2.0` as suggested above and simply use the pipeline. For example:

       import torch
       from diffusers import StableDiffusionPipeline

       pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
       pipe = pipe.to("cuda")

       prompt = "a photo of an astronaut riding a horse on mars"
       image = pipe(prompt).images[0]

   If you want to enable it explicitly (which is not required), you can do so as shown below.

       import torch
       from diffusers import StableDiffusionPipeline
       from diffusers.models.cross_attention import AttnProcessor2_0

       pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
       pipe.unet.set_attn_processor(AttnProcessor2_0())

       prompt = "a photo of an astronaut riding a horse on mars"
       image = pipe(prompt).images[0]

   This should be as fast and memory efficient as `xFormers`. More details in our benchmark.

2. torch.compile

   To get an additional speedup, we can use the new `torch.compile` feature. To do so, we simply wrap our `unet` with `torch.compile`. For more information and different options, refer to the torch compile docs.

       import torch
       from diffusers import StableDiffusionPipeline

       pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
           "cuda"
       )
       pipe.unet = torch.compile(pipe.unet)

       batch_size = 10
       prompt = "A photo of an astronaut riding a horse on marse."
       images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images

   Depending on the type of GPU, `compile()` can yield between 2-9% of additional speed-up over the accelerated transformer optimizations. Note, however, that compilation is able to squeeze more performance improvements in more recent GPU architectures such as Ampere (A100, 3090), Ada (4090) and Hopper (H100).

   Compilation takes some time to complete, so it is best suited for situations where you need to prepare your pipeline once and then perform the same type of inference operations multiple times.

##
Benchmark

We conducted a simple benchmark on different GPUs to compare vanilla attention, xFormers, `torch.nn.functional.scaled_dot_product_attention` and `torch.compile+torch.nn.functional.scaled_dot_product_attention`. For the benchmark we used the the stable-diffusion-v1-4 model with 50 steps. The `xFormers` benchmark is done using the `torch==1.13.1` version, while the accelerated transformers optimizations are tested using nightly versions of PyTorch 2.0. The tables below summarize the results we got.

The `Speed over xformers` columns denote the speed-up gained over `xFormers` using the `torch.compile+torch.nn.functional.scaled_dot_product_attention`.

###
FP16 benchmark

The table below shows the benchmark results for inference using `fp16`. As we can see, `torch.nn.functional.scaled_dot_product_attention` is as fast as `xFormers` (sometimes slightly faster/slower) on all the GPUs we tested. And using `torch.compile` gives further speed-up of up of 10% over `xFormers`, but it’s mostly noticeable on the A100 GPU.

The time reported is in seconds.

GPU | Batch Size | Vanilla Attention | xFormers | PyTorch2.0 SDPA | SDPA + torch.compile | Speed over xformers (%)
--- | --- | --- | --- | --- | --- | ---
A100 | 10 | 12.02 | 8.7 | 8.79 | 7.89 | 9.31
A100 | 16 | 18.95 | 13.57 | 13.67 | 12.25 | 9.73
A100 | 32 (1) | OOM | 26.56 | 26.68 | 24.08 | 9.34
A100 | 64 |  | 52.51 | 53.03 | 47.81 | 8.95
A10 | 4 | 13.94 | 9.81 | 10.01 | 9.35 | 4.69
A10 | 8 | 27.09 | 19 | 19.53 | 18.33 | 3.53
A10 | 10 | 33.69 | 23.53 | 24.19 | 22.52 | 4.29
A10 | 16 | OOM | 37.55 | 38.31 | 36.81 | 1.97
A10 | 32 (1) |  | 77.19 | 78.43 | 76.64 | 0.71
A10 | 64 (1) |  | 173.59 | 158.99 | 155.14 | 10.63
T4 | 4 | 38.81 | 30.09 | 29.74 | 27.55 | 8.44
T4 | 8 | OOM | 55.71 | 55.99 | 53.85 | 3.34
T4 | 10 | OOM | 68.96 | 69.86 | 65.35 | 5.23
T4 | 16 | OOM | 111.47 | 113.26 | 106.93 | 4.07
V100 | 4 | 9.84 | 8.16 | 8.09 | 7.65 | 6.25
V100 | 8 | OOM | 15.62 | 15.44 | 14.59 | 6.59
V100 | 10 | OOM | 19.52 | 19.28 | 18.18 | 6.86
V100 | 16 | OOM | 30.29 | 29.84 | 28.22 | 6.83
3090 | 4 | 10.04 | 7.82 | 7.89 | 7.47 | 4.48
3090 | 8 | 19.27 | 14.97 | 15.04 | 14.22 | 5.01
3090 | 10 | 24.08 | 18.7 | 18.7 | 17.69 | 5.40
3090 | 16 | OOM | 29.06 | 29.06 | 28.2 | 2.96
3090 | 32 (1) |  | 58.05 | 58 | 54.88 | 5.46
3090 | 64 (1) |  | 126.54 | 126.03 | 117.33 | 7.28
3090 Ti | 4 | 9.07 | 7.14 | 7.15 | 6.81 | 4.62
3090 Ti | 8 | 17.51 | 13.65 | 13.72 | 12.99 | 4.84
3090 Ti | 10 (2) | 21.79 | 16.85 | 16.93 | 16.02 | 4.93
3090 Ti | 16 | OOM | 26.1 | 26.28 | 25.46 | 2.45
3090 Ti | 32 (1) |  | 51.78 | 52.04 | 49.15 | 5.08
3090 Ti | 64 (1) |  | 112.02 | 112.33 | 103.91 | 7.24
4090 | 4 | 10.48 | 8.37 | 8.32 | 8.01 | 4.30
4090 | 8 | 14.33 | 10.22 | 10.42 | 9.78 | 4.31
4090 | 16 |  | 17.07 | 17.46 | 17.15 | -0.47
4090 | 32 (1) |  | 39.03 | 39.86 | 37.97 | 2.72
4090 | 64 (1) |  | 77.29 | 79.44 | 77.67 | -0.49

###
FP32 benchmark

The table below shows the benchmark results for inference using `fp32`. In this case, `torch.nn.functional.scaled_dot_product_attention` is faster than `xFormers` on all the GPUs we tested.

Using `torch.compile` in addition to the accelerated transformers implementation can yield up to 19% performance improvement over `xFormers` in Ampere and Ada cards, and up to 20% (Ampere) or 28% (Ada) over vanilla attention.

GPU | Batch Size | Vanilla Attention | xFormers | PyTorch2.0 SDPA | SDPA + torch.compile | Speed over xformers (%) | Speed over vanilla (%)
--- | --- | --- | --- | --- | --- | --- | ---
A100 | 4 | 16.56 | 12.42 | 12.2 | 11.84 | 4.67 | 28.50
A100 | 10 | OOM | 29.93 | 29.44 | 28.5 | 4.78 |
A100 | 16 |  | 47.08 | 46.27 | 44.8 | 4.84 |
A100 | 32 |  | 92.89 | 91.34 | 88.35 | 4.89 |
A100 | 64 |  | 185.3 | 182.71 | 176.48 | 4.76 |
A10 | 1 | 10.59 | 8.81 | 7.51 | 7.35 | 16.57 | 30.59
A10 | 4 | 34.77 | 27.63 | 22.77 | 22.07 | 20.12 | 36.53
A10 | 8 |  | 56.19 | 43.53 | 43.86 | 21.94 |
A10 | 16 |  | 116.49 | 88.56 | 86.64 | 25.62 |
A10 | 32 |  | 221.95 | 175.74 | 168.18 | 24.23 |
A10 | 48 |  | 333.23 | 264.84 |  | 20.52 |
T4 | 1 | 28.2 | 24.49 | 23.93 | 23.56 | 3.80 | 16.45
T4 | 2 | 52.77 | 45.7 | 45.88 | 45.06 | 1.40 | 14.61
T4 | 4 | OOM | 85.72 | 85.78 | 84.48 | 1.45 |
T4 | 8 |  | 149.64 | 150.75 | 148.4 | 0.83 |
V100 | 1 | 7.4 | 6.84 | 6.8 | 6.66 | 2.63 | 10.00
V100 | 2 | 13.85 | 12.81 | 12.66 | 12.35 | 3.59 | 10.83
V100 | 4 | OOM | 25.73 | 25.31 | 24.78 | 3.69 |
V100 | 8 |  | 43.95 | 43.37 | 42.25 | 3.87 |
V100 | 16 |  | 84.99 | 84.73 | 82.55 | 2.87 |
3090 | 1 | 7.09 | 6.78 | 6.11 | 6.03 | 11.06 | 14.95
3090 | 4 | 22.69 | 21.45 | 18.67 | 18.09 | 15.66 | 20.27
3090 | 8 |  | 42.59 | 36.75 | 35.59 | 16.44 |
3090 | 16 |  | 85.35 | 72.37 | 70.25 | 17.69 |
3090 | 32 (1) |  | 162.05 | 138.99 | 134.53 | 16.98 |
3090 | 48 |  | 241.91 | 207.75 |  | 14.12 |
3090 Ti | 1 | 6.45 | 6.19 | 5.64 | 5.49 | 11.31 | 14.88
3090 Ti | 4 | 20.32 | 19.31 | 16.9 | 16.37 | 15.23 | 19.44
3090 Ti | 8 (2) |  | 37.93 | 33.05 | 31.99 | 15.66 |
3090 Ti | 16 |  | 75.37 | 65.25 | 64.32 | 14.66 |
3090 Ti | 32 (1) |  | 142.55 | 124.44 | 120.74 | 15.30 |
3090 Ti | 48 |  | 213.19 | 186.55 |  | 12.50 |
4090 | 1 | 5.54 | 4.99 | 4.51 | 4.44 | 11.02 | 19.86
4090 | 4 | 13.67 | 11.4 | 10.3 | 9.84 | 13.68 | 28.02
4090 | 8 |  | 19.79 | 17.13 | 16.19 | 18.19 |
4090 | 16 |  | 38.62 | 33.14 | 32.31 | 16.34 |
4090 | 32 (1) |  | 76.57 | 65.96 | 62.05 | 18.96 |
4090 | 48 |  | 114.44 | 98.78 |  | 13.68 |

(1) Batch Size >= 32 requires enable_vae_slicing() because of https://github.com/pytorch/pytorch/issues/81665 This is required for PyTorch 1.13.1, and also for PyTorch 2.0 and batch size of 64

For more details about how this benchmark was run, please refer to this PR.