Revert "Add xdit diffusion docs (#5576)" (#5580)

This reverts commit 4132a2609c.

.wordlist.txt

@@ -220,7 +220,6 @@ href
 Hyperparameters
 HybridEngine
 Huggingface
-Hunyuan
 IB
 ICD
 ICT
@@ -532,7 +531,6 @@ UAC
 UC
 UCC
 UCX
-ud
 UE
 UIF
 UMC
@@ -844,7 +842,6 @@ pallas
 parallelization
 parallelizing
 param
-params
 parameterization
 passthrough
 pe
@@ -891,7 +888,6 @@ querySelectorAll
 queueing
 qwen
 radeon
-rc
 rccl
 rdc
 rdma
@@ -1056,8 +1052,6 @@ writebacks
 wrreq
 wzo
 xargs
-xdit
-xDiT
 xGMI
 xPacked
 xz

docs/conf.py

@@ -175,7 +175,6 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.10.0-20250812", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/sglang-history", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/inference/xdit-video-diffusion", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/inference-optimization/index", "os": ["linux"]},

docs/data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml

@@ -1,38 +0,0 @@
-xdit_video_diffusion:
-  docker:
-    pull_tag: amdsiloai/pytorch-xdit:v25.9
-    docker_hub_url: https://hub.docker.com/r/amdsiloai/pytorch-xdit
-    ROCm: 7.0.0rc
-    components:
-      TheRock: 7afbe45
-      rccl: 9b04b2a
-      composable_kernel: b7a806f
-      rocm-libraries: f104555
-      rocm-systems: 25922d0
-      torch: 2.10.0a0+git3caf6da
-      torchvision: 0.22.0a0+966da7e
-      triton: 3.5.0+gitea06d636
-       
-  model_groups:
-    - group: Hunyuan Video
-      tag: hunyuan
-      models:
-        - model: Hunyuan Video
-          model_name: hunyuanvideo
-          model_repo: tencent/HunyuanVideo
-          revision: refs/pr/18
-          url: https://huggingface.co/tencent/HunyuanVideo
-          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
-    - group: Wan-AI
-      tag: wan
-      models:
-        - model: Wan2.1
-          model_name: wan2.1_i2v_14b_720p
-          model_repo: Wan-AI/Wan2.1-I2V-14B-720P
-          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P
-          github: https://github.com/Wan-Video/Wan2.1
-        - model: Wan2.2
-          model_name: wan2.2-i2v-a14b
-          model_repo: Wan-AI/Wan2.2-I2V-A14B
-          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B
-          github: https://github.com/Wan-Video/Wan2.2

docs/how-to/rocm-for-ai/inference/index.rst

@@ -26,6 +26,4 @@ training, fine-tuning, and inference. It leverages popular machine learning fram
 
 - :doc:`SGLang inference performance testing <benchmark-docker/sglang>`
 
-- :doc:`xDiT video inference <xdit-video-diffusion>`
-
 - :doc:`Deploying your model <deploy-your-model>`

docs/how-to/rocm-for-ai/inference/xdit-video-diffusion.rst

@@ -1,322 +0,0 @@
-.. meta::
-   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
-                 prebuilt and optimized docker images.
-   :keywords: xDiT, diffusion, video, video generation, validate, benchmark
-
-********************
-xDiT video inference
-********************
-
-.. _xdit-video-diffusion:
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
-
-   {% set docker = data.xdit_video_diffusion.docker %}
-   {% set model_groups = data.xdit_video_diffusion.model_groups%}
-
-   The `amdsiloai/pytorch-xdit Docker <{{ docker.docker_hub_url }}>`_ image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
-   benchmarking diffusion model video generation on
-   AMD Instinct™ MI355X, MI350X (gfx950), and MI300X GPUs.
-   The image runs ROCm `{{docker.ROCm}}` based on `TheRock <https://github.com/ROCm/TheRock>`_
-   and includes the following components:
-
-   .. tab-set::
-
-      .. tab-item:: {{ docker.pull_tag }}
-
-         .. list-table::
-            :header-rows: 1
-
-            * - Software component
-              - Version
-
-            {% for component_name, component_version in docker.components.items() %}
-            * - {{ component_name }}
-              - {{ component_version }}
-            {% endfor %}
-
-Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
-
-What's new
-==========
-
-- Initial release
-- ROCm: 7.0.0rc
-- Added support for AMD Instinct™ MI355X, MI350X (gfx950), and MI300X (gfx942) GPUs.
-- Added support for Wan 2.1, Wan 2.2 and Hunyuan Video models with MIOpen optimizations.
-
-.. _xdit-video-diffusion-supported-models:
-
-Supported models
-================
-
-The following models are supported for inference performance benchmarking.
-Some instructions, commands, and recommendations in this documentation might
-vary by model -- select one to get started.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
-
-   {% set docker = data.xdit_video_diffusion.docker %}
-   {% set model_groups = data.xdit_video_diffusion.model_groups%}
-
-   .. raw:: html
-
-      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
-          <div class="row gx-0">
-              <div class="col-2 me-1 px-2 model-param-head">Model</div>
-              <div class="row col-10 pe-0">
-        {% for model_group in model_groups %}
-                  <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
-        {% endfor %}
-              </div>
-          </div>
-
-          <div class="row gx-0 pt-1">
-              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
-              <div class="row col-10 pe-0">
-        {% for model_group in model_groups %}
-            {% set models = model_group.models %}
-            {% for model in models %}
-                {% if models|length % 3 == 0 %}
-                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.model_name }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-                {% else %}
-                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.model_name }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
-                {% endif %}
-            {% endfor %}
-        {% endfor %}
-              </div>
-          </div>
-      </div>
-
-   {% for model_group in model_groups %}
-       {% for model in model_group.models %}
-
-   .. container:: model-doc {{model.model_name}}
-
-      .. note::
-
-         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
-         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
-         external license agreement through a third party.
-
-       {% endfor %}
-   {% endfor %}
-
-System validation
-=================
-
-Before running AI workloads, it's important to validate that your AMD hardware is configured
-correctly and performing optimally.
-
-If you have already validated your system settings, including aspects like NUMA auto-balancing, you
-can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
-optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
-before starting.
-
-To test for optimal performance, consult the recommended :ref:`System health benchmarks
-<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
-system's configuration.
-
-Pull the Docker image
-=====================
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
-
-   {% set docker = data.xdit_video_diffusion.docker %}
-
-   For this tutorial, it's recommended to use the ``{{ docker.pull_tag }}`` Docker image.
-   Pull the image using the following command:
-
-   .. code-block:: shell
-
-      docker pull {{ docker.pull_tag }}
-
-Validate and benchmark
-======================
-
-Once the image has been downloaded you can follow these steps to
-run benchmarks and generate a video.
-
-.. warning::
-
-   If your host/OS ROCm installation is below 6.4.2 (see with ``apt show rocm-libs``) you need to export
-   the ``HSA_NO_SCRATCH_RECLAIM=1`` environment variable inside the container, or the workload will crash.
-   If possible, ask your system administrator to upgrade ROCm.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
-
-   {% for model_group in model_groups %}
-     {% for model in model_group.models %}
-
-   .. container:: model-doc {{model.model_name}}
-
-      The following commands are written for {{ model.model }}.
-      See :ref:`xdit-video-diffusion-supported-models` to switch to another available model.
-
-     {% endfor %}
-   {% endfor %}
-
-Choose your setup method
-------------------------
-
-You can either use an existing Hugging Face cache or download the model fresh inside the container.
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
-
-   {% set docker = data.xdit_video_diffusion.docker %}
-   {% set model_groups = data.xdit_video_diffusion.model_groups%}
-
-   {% for model_group in model_groups %}
-     {% for model in model_group.models %}
-   .. container:: model-doc {{model.model_name}}
-
-      .. tab-set::
-
-         .. tab-item:: Option 1: Use existing Hugging Face cache
-
-            If you already have models downloaded on your host system, you can mount your existing cache.
-
-            1. Set your Hugging Face cache location.
-
-               .. code-block:: shell
-
-                  export HF_HOME=/your/hf_cache/location
-
-            2. Download the model (if not already cached).
-
-               .. code-block:: shell
-
-                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
-
-            3. Launch the container with mounted cache.
-
-               .. code-block:: shell
-
-                  docker run \
-                      -it --rm \
-                      --cap-add=SYS_PTRACE \
-                      --security-opt seccomp=unconfined \
-                      --user root \
-                      --device=/dev/kfd \
-                      --device=/dev/dri \
-                      --group-add video \
-                      --ipc=host \
-                      --network host \
-                      --privileged \
-                      --shm-size 128G \
-                      --name pytorch-xdit \
-                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-                      -e HF_HOME=/app/huggingface_models \
-                      -v $HF_HOME:/app/huggingface_models \
-                      {{ docker.pull_tag }}
-
-         .. tab-item:: Option 2: Download inside container
-
-            If you prefer to keep the container self-contained or don't have an existing cache.
-
-            1. Launch the container
-
-               .. code-block:: shell
-
-                  docker run \
-                      -it --rm \
-                      --cap-add=SYS_PTRACE \
-                      --security-opt seccomp=unconfined \
-                      --user root \
-                      --device=/dev/kfd \
-                      --device=/dev/dri \
-                      --group-add video \
-                      --ipc=host \
-                      --network host \
-                      --privileged \
-                      --shm-size 128G \
-                      --name pytorch-xdit \
-                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-                      {{ docker.pull_tag }}
-
-            2. Inside the container, set the Hugging Face cache location and download the model.
-
-               .. code-block:: shell
-
-                  export HF_HOME=/your/hf_cache/location
-                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
-
-               .. warning::
-
-                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
-     {% endfor %}
-   {% endfor %}
-
-Run inference
-=============
-
-.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
-
-   {% set model_groups = data.xdit_video_diffusion.model_groups%}
-   {% for model_group in model_groups %}
-     {% for model in model_group.models %}
-
-   .. container:: model-doc {{ model.model_name }}
-
-      To run the benchmarks for {{ model.model }}, use the following command:
-
-      .. code-block:: shell
-       {% if model.model == "Hunyuan Video" %}
-         cd /app/Hunyuanvideo
-         mkdir results
-
-         torchrun --nproc_per_node=8 run.py \
-             --model tencent/HunyuanVideo \
-             --prompt "In the large cage, two puppies were wagging their tails at each other." \
-             --height 720 --width 1280 --num_frames 129 \
-             --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
-             --ulysses_degree 8 \
-             --enable_tiling --enable_slicing \
-             --use_torch_compile \
-             --bench_output results
-       {% endif %}
-       {% if model.model == "Wan2.1" %}
-         cd Wan2.1
-         mkdir results
-
-         torchrun --nproc_per_node=8 run.py \
-             --task i2v-14B \
-             --size 720*1280 --frame_num 81 \
-             --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.1-I2V-14B-720P/snapshots/8823af45fcc58a8aa999a54b04be9abc7d2aac98/" \
-             --image "/app/Wan2.1/examples/i2v_input.JPG" \
-             --ulysses_size 8 --ring_size 1 \
-             --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." \
-             --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
-             --offload_model 0 \
-             --vae_dtype bfloat16
-       {% endif %}
-       {% if model.model == "Wan2.2" %}
-         cd Wan2.2
-         mkdir results
-
-         torchrun --nproc_per_node=8 run.py \
-             --task i2v-A14B \
-             --size 720*1280 --frame_num 81 \
-             --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.2-I2V-A14B/snapshots/206a9ee1b7bfaaf8f7e4d81335650533490646a3/" \
-             --image "/app/Wan2.2/examples/i2v_input.JPG" \
-             --ulysses_size 8 --ring_size 1 \
-             --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." \
-             --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
-             --offload_model 0 \
-             --vae_dtype bfloat16
-       {% endif %}
-
-      {% if model.model in ["Wan2.1", "Wan2.2"] %}
-      For additional performance improvements, consider adding the ``--compile`` flag to the above command. Note that this can significantly increase startup time on the first call.
-      {% endif %}
-
-      The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% endif %}
-
-      {% endfor %}
-    {% endfor %}
-
-Known limitations
-=================
-
-- OOB tuning: Currently only Instinct MI300X has been tuned for in the gfx942
-  series. Other gfx942 GPUs might not perform optimally out-of-the-box.

docs/sphinx/_toc.yml.in

@@ -117,8 +117,6 @@ subtrees:
             title: SGLang inference performance testing
           - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
             title: SGLang distributed inference with Mooncake
-          - file: how-to/rocm-for-ai/inference/xdit-video-diffusion.rst
-            title: xDiT video inference
           - file: how-to/rocm-for-ai/inference/deploy-your-model.rst
             title: Deploy your model