[docs] AWS Trainium: rewrite guide for native TorchNeuron support

docs/source/en/_toctree.yml

@@ -115,9 +115,9 @@
     - local: optimization/habana
       title: Intel Gaudi
     - local: optimization/neuron
-      title: AWS Neuron
+      title: AWS Trainium
     - local: optimization/tpu
-      title: TPU
+      title: Google TPU
     title: Hardware-specific acceleration
   - isExpanded: false
     sections:

docs/source/en/optimization/neuron.md

@@ -1,4 +1,4 @@
-<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -10,49 +10,108 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# AWS Neuron
+# AWS Trainium
 
-Diffusers functionalities are available on [AWS Inf2 instances](https://aws.amazon.com/ec2/instance-types/inf2/), which are EC2 instances powered by [Neuron machine learning accelerators](https://aws.amazon.com/machine-learning/inferentia/). These instances aim to provide better compute performance (higher throughput, lower latency) with good cost-efficiency, making them good candidates for AWS users to deploy diffusion models to production.
+[TorchNeuron](https://github.com/aws-neuron/torch-neuronx) is the native PyTorch backend for Trainium2 and Trainium3. Diffusers runs on these instances through the `torch_neuronx` package, which registers a `"neuron"` device, so pipelines run with the same code as on any other device.
 
-[Optimum Neuron](https://huggingface.co/docs/optimum-neuron/en/index) is the interface between Hugging Face libraries and AWS Accelerators, including AWS [Trainium](https://aws.amazon.com/machine-learning/trainium/) and AWS [Inferentia](https://aws.amazon.com/machine-learning/inferentia/). It supports many of the features in Diffusers with similar APIs, so it is easier to learn if you're already familiar with Diffusers. Once you have created an AWS Inf2 instance, install Optimum Neuron.
+Two execution modes are available:
 
-```bash
-python -m pip install --upgrade-strategy eager optimum[neuronx]
+| Mode | How to activate | Notes |
+|---|---|---|
+| Eager | `pipe.to("neuron")` | Operations dispatched one at a time, asynchronous |
+| Compile | `torch.compile(module, backend="neuron")` | Whole-graph compilation, static shapes |
+
+Follow the [AWS Neuron documentation](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) to set up the Neuron
+SDK and install `torch_neuronx`. Once installed, `import torch` loads it automatically and registers the `"neuron"`
+device, so `pipe.to("neuron")` is the only change needed. Add `import torch_neuronx` only if you disabled backend
+autoloading with `TORCH_DEVICE_BACKEND_AUTOLOAD=0`.
+
+In eager mode, each operation compiles the first time it runs. In compiled mode, the first call compiles the graph. Both modes cache the compiled Neuron executable format (NEFF) under `tmp/neff_cache` by default, and that directory is cleared on reboot. Set `TORCH_NEURONX_NEFF_CACHE_DIR` to a persistent directory to keep the cache.
+
+## Eager mode
+
+FLUX.2-klein-4B fits on a single NeuronCore, so move the whole pipeline to the `"neuron"` device.
+
+```python
+import torch
+
+from diffusers import Flux2KleinPipeline
+
+pipe = Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-4B", dtype=torch.bfloat16)
+pipe.to("neuron")
+
+image = pipe(
+    prompt="a golden retriever surfing a wave, photorealistic",
+    height=1024,
+    width=1024,
+    num_inference_steps=4,
+).images[0]
+
+image.save("output.png")
 ```
 
-> [!TIP]
-> We provide pre-built [Hugging Face Neuron Deep Learning AMI](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2) (DLAMI) and Optimum Neuron containers for Amazon SageMaker. It's recommended to correctly set up your environment.
+If a model is too large for a single NeuronCore, or you have several cores and want lower latency, shard the models
+across cores instead. See the [Tensor parallelism](#tensor-parallelism) section.
 
-The example below demonstrates how to generate images with the Stable Diffusion XL model on an inf2.8xlarge instance (you can switch to cheaper inf2.xlarge instances once the model is compiled). To generate some images, use the [`~optimum.neuron.NeuronStableDiffusionXLPipeline`] class, which is similar to the [`StableDiffusionXLPipeline`] class in Diffusers.
+## Compiled mode
 
-Unlike Diffusers, you need to compile models in the pipeline to the Neuron format, `.neuron`. Launch the following command to export the model to the `.neuron` format.
+TorchNeuron registers `"neuron"` as a `torch.compile` backend name, so components compile like any other
+`torch.compile` target. The first call (warmup) is slow because it compiles; later calls with the same shapes reuse the compiled graph.
 
-```bash
-optimum-cli export neuron --model stabilityai/stable-diffusion-xl-base-1.0 \
-  --batch_size 1 \
-  --height 1024 `# height in pixels of generated image, eg. 768, 1024` \
-  --width 1024 `# width in pixels of generated image, eg. 768, 1024` \
-  --num_images_per_prompt 1 `# number of images to generate per prompt, defaults to 1` \
-  --auto_cast matmul `# cast only matrix multiplication operations` \
-  --auto_cast_type bf16 `# cast operations from FP32 to BF16` \
-  sd_neuron_xl/
+> [!IMPORTANT]
+> TorchNeuron requires *static shapes*, so pass `dynamic=False`. A new `height` or `width` compiles again for that shape. Shapes already seen are reused. Changing `num_inference_steps` doesn't recompile.
+
+```python
+import torch
+
+from diffusers import Flux2KleinPipeline
+
+pipe = Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-4B", dtype=torch.bfloat16)
+pipe.to("neuron")
+pipe.transformer.compile(backend="neuron", fullgraph=True, dynamic=False)
+
+# Warmup — triggers static graph compilation.
+pipe(prompt="warmup", height=1024, width=1024, num_inference_steps=4)
+
+# Later calls with the same shapes reuse the compiled graph.
+image = pipe(
+    prompt="a golden retriever surfing a wave, photorealistic",
+    height=1024,
+    width=1024,
+    num_inference_steps=4,
+).images[0]
+
+image.save("output.png")
 ```
 
-Now generate some images with the pre-compiled SDXL model.
+For transformers with repeated blocks, use `compile_repeated_blocks(...)` to compile those blocks individually. This block is reused and reduces compile time considerably.
+
+## Tensor parallelism
+
+Follow the [Tensor parallelism](../training/distributed_inference#tensor-parallelism) guide for model loading,
+sharding plans, and limitations. To adapt its example for Neuron, replace the CUDA process-group and device setup
+with the following.
+
+On Neuron, initialize the process group with `backend="neuron"` and build the mesh with `DeviceMesh("neuron", ...)`.
 
 ```python
->>> from optimum.neuron import NeuronStableDiffusionXLPipeline
+import torch
+import torch.distributed as dist
+from torch.distributed.device_mesh import DeviceMesh
+
+from diffusers import TensorParallelConfig
 
->>> stable_diffusion_xl = NeuronStableDiffusionXLPipeline.from_pretrained("sd_neuron_xl/")
->>> prompt = "a pig with wings flying in floating US dollar banknotes in the air, skyscrapers behind, warm color palette, muted colors, detailed, 8k"
->>> image = stable_diffusion_xl(prompt).images[0]
+dist.init_process_group(backend="neuron")
+mesh = DeviceMesh("neuron", list(range(dist.get_world_size())))
+device = torch.neuron.current_device()
+parallel_config = TensorParallelConfig(mesh=mesh)
 ```
 
-<img
-  src="https://huggingface.co/datasets/Jingya/document_images/resolve/main/optimum/neuron/sdxl_pig.png"
-  width="256"
-  height="256"
-  alt="peggy generated by sdxl on inf2"
-/>
+Pass this `parallel_config` to the transformer's [`~ModelMixin.from_pretrained`] and use `device` for the remaining
+pipeline components.
 
-Feel free to check out more guides and examples on different use cases from the Optimum Neuron [documentation](https://huggingface.co/docs/optimum-neuron/en/inference_tutorials/stable_diffusion#generate-images-with-stable-diffusion-models-on-aws-inferentia)!
+Launch one process per NeuronCore. Set `--nproc_per_node` to the number of NeuronCores you want to shard across.
+
+```bash
+torchrun --nproc_per_node=8 tensor_parallel_neuron.py
+```

docs/source/en/optimization/tpu.md

@@ -10,16 +10,18 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# TorchTPU
+# Google TPU
 
-[TorchTPU](https://github.com/google-pytorch/torch_tpu/) is a PyTorch backend for Google's Tensor Processing Units
-(TPUs), which lets you run Diffusers pipelines on Cloud TPUs (v6e, v7x) with minimal code changes.
+Tensor Processing Units (TPUs) are Google's machine learning accelerators, available as Cloud TPUs (v6e, v7x).
+Diffusers runs on them through [TorchTPU](https://github.com/google-pytorch/torch_tpu/), the PyTorch backend for TPUs,
+with minimal code changes.
 
-Two execution modes are available:
+Three execution modes are available:
 
 | Mode | Constant | How to activate | Notes |
 |---|---|---|---|
 | Strict eager (default) | `EagerMode.DEFER_NEVER` | `pipe.to("tpu")` | Operations dispatched one at a time, asynchronous |
+| Optimized eager | `EagerMode.DEFER_AND_FUSE` | `TORCH_TPU_DEFER_AND_FUSE=1` | Operations deferred and fused into larger graphs; faster than strict eager, longer compile |
 | Compile | — | `torch.compile(module, backend="tpu")` | AOT compilation with `TpuBackend` |
 
 Follow the [TorchTPU installation guide](https://github.com/google-pytorch/torch_tpu/). Once installed, `import torch`