Add 4090 benchmark (PyTorch 2.0) (#2503)

* Add 4090 benchmark (PyTorch 2.0)

* Small changes in nomenclature.

Author: pcuenca

docs/source/en/optimization/torch2.0.mdx

@@ -10,29 +10,29 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Torch2.0 support in Diffusers
+# Accelerated PyTorch 2.0 support in Diffusers
 
 Starting from version `0.13.0`, Diffusers supports the latest optimization from the upcoming [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/) release. These include:
-1. Support for native flash and memory-efficient attention without any extra dependencies.
-2. [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) support for compiling individual models for extra performance boost.
+1. Support for accelerated transformers implementation with memory-efficient attention – no extra dependencies required.
+2. [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) support for extra performance boost when individual models are compiled.
 
 
 ## Installation
-To benefit from the native efficient attention 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 CUDA11.7 or CUDA11.8, 
-as torch2.0 does not support the previous versions. Once CUDA is installed, torch nightly can be installed using:
+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:
 
 ```bash
 pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu117
 ```
 
-## Using efficient attention and torch.compile.
+## Using accelerated transformers and torch.compile.
 
 
-1. **Efficient Attention**
+1. **Accelerated Transformers implementation**
 
-   Efficient attention is implemented via the [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention) function, which automatically enables flash/memory efficient attention, depending on the input and the GPU type. This is the same as the `memory_efficient_attention` from [xFormers](https://github.com/facebookresearch/xformers) but built natively into PyTorch. 
+   PyTorch 2.0 includes an optimized and memory-efficient attention implementation through the [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/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](https://github.com/facebookresearch/xformers), but built natively into PyTorch. 
 
-   Efficient attention will be enabled by default in Diffusers if torch2.0 is installed and if `torch.nn.functional.scaled_dot_product_attention` is available. To use it, you can install torch2.0 as suggested above and use the pipeline. For example:
+   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:
 
     ```Python
     import torch
@@ -59,12 +59,12 @@ pip install --pre torch torchvision --index-url https://download.pytorch.org/whl
     image = pipe(prompt).images[0]
     ```
 
-    This should be as fast and memory efficient as `xFormers`.
+    This should be as fast and memory efficient as `xFormers`. More details [in our benchmark](#benchmark).
 
 
 2. **torch.compile**
 
-    To get an additional speedup, we can use the new `torch.compile` feature. To do so, we wrap our `unet` with `torch.compile`. For more information and different options, refer to the 
+    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](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html).
 
     ```python
@@ -81,22 +81,23 @@ pip install --pre torch torchvision --index-url https://download.pytorch.org/whl
     images = pipe(prompt, num_inference_steps=steps, num_images_per_prompt=batch_size).images
     ```
 
-    Depending on the type of GPU it can give between 2-9% speed-up over efficient attention. But note that as of now the speed-up is mostly noticeable on the more recent GPU architectures, such as in the A100.
+    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).
 
-    Note that compilation will also take 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.
+    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](https://huggingface.co/CompVis/stable-diffusion-v1-4) model with 50 steps. `xFormers` benchmark is done using the `torch==1.13.1` version. The table below summarizes the result that we got.
-The `Speed over xformers` columns denotes the speed-up gained over `xFormers` using the `torch.compile+torch.nn.functional.scaled_dot_product_attention`.
+For the benchmark we used the the [stable-diffusion-v1-4](https://huggingface.co/CompVis/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 up to 10% over `xFormers`, but it's mostly noticeable on the A100 GPU.
+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.___
 
@@ -105,7 +106,7 @@ ___The time reported is in seconds.___
 | 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(2) | | 52.51 | 53.03 | 47.81 | 8.95 |
+| 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 |
@@ -137,13 +138,20 @@ ___The time reported is in seconds.___
 | 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`. 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.
-Using `torch.compile` with efficient attention gives up to 18% performance improvement over `xFormers` in Ampere cards, and up to 20% over vanilla attention.
+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 (%) |
 | --- | --- | --- | --- | --- | --- | --- | --- |
@@ -173,7 +181,7 @@ Using `torch.compile` with efficient attention gives up to 18% performance impro
 | | | | | | | |
 | 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 (2) | | 42.59 | 36.75 | 35.59 | 16.44 | |
+| 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 | |
@@ -185,12 +193,12 @@ Using `torch.compile` with efficient attention gives up to 18% performance impro
 | 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 | | | |
-| 4090 | 4 | 13.67 | 11.4 | 10.3 | | | |
-| 4090 | 8 (2) | | 19.79 | 17.13 | | | |
-| 4090 | 16 | | 38.62 | 33.14 | | | |
-| 4090 | 32 (1) | | 76.57 | 65.96 | | | |
-| 4090 | 48 | | 114.44 | 98.78 | | | |
+| 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 | |