43 lines
2.1 KiB
Markdown
43 lines
2.1 KiB
Markdown
# AMD and ROCm
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FastVideo has a ROCm platform implementation for AMD GPUs. This page describes
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the checked-in support paths; a complete device-by-model validation matrix and
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end-to-end installation walkthrough are not currently documented.
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## What is implemented
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- ROCm platform detection uses AMD SMI or a HIP-enabled PyTorch build with
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access to `/dev/kfd` and DRM render devices.
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- The platform supports PyTorch SDPA and selects FlashAttention when its
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installation, dtype and head size are compatible, with SDPA fallback.
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- `VIDEO_SPARSE_ATTN` and `VIDEO_SPARSE_ATTN_H3` use the kernel package's Triton
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routes. CUDA CuTe and ThunderKittens paths are not ROCm implementations.
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- The optimization guide documents a CDNA4-specific
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[FP8 weight path](../../inference/optimizations.md#fp8-weight-quantization).
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This does not establish FP8 support on every AMD device.
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These paths are defined in the
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[ROCm platform source](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/platforms/rocm.py)
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and [platform detection](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/platforms/__init__.py).
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## Setup references
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The repository includes a
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[ROCm Dockerfile](https://github.com/hao-ai-lab/FastVideo/blob/main/docker/rocm7_1.Dockerfile)
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and [kernel build instructions](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo-kernel/README.md#rocm-build).
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The kernel build supports `./build.sh --rocm` and a Python/Triton-only mode when
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the compiled HIP extension is unavailable.
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Use a matching ROCm-enabled PyTorch environment. The ordinary CUDA installation
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command is not an AMD installation command. Check the
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[support matrix](../../inference/support_matrix.md) and the selected model's
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[cookbook](../../cookbook/index.md) for its runtime requirements. A CUDA recipe
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or released NVFP4 checkpoint alone is not evidence of an AMD-compatible run.
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## Documentation still needed
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A maintained ROCm walkthrough should record the GPU architecture, ROCm/PyTorch
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versions, model revision, attention backend, runnable command, output, and measured
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memory. Until that evidence is published, device and model combinations beyond
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the linked source paths remain unverified here.
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