Add cu130-torch213 dependency group (torch 2.13 on CUDA 13.0) - #134
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Add cu130-torch213 dependency group (torch 2.13 on CUDA 13.0)#134pengcuo wants to merge 2 commits into
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Introduce `cu130-torch213` and `cu130-torch213-train` groups pinning torch==2.13.0+cu130 (with matching torchvision 0.28.0, torchcodec 0.14.0, torchao 0.17.0, triton 3.7.1, natten 0.21.6+cu130.torch213 on aarch64, and the cu13 NVIDIA runtime libs), plus a conflicts entry so it is mutually exclusive with the other backend groups. Regenerates uv.lock for the new closure. torchvision 0.28 removed `torchvision.io.read_video`, so switch inference/vision.py to a torchcodec-based THWC uint8 decoder (forced onto CPU so an active default-CUDA context during generation doesn't route torchcodec's internal index tensor to CUDA). Bump the recommended NGC base image to nvcr.io/nvidia/pytorch:26.06-py3 in README and docs/setup.md to match the torch 2.13 line. Verified on 4x GB200 (aarch64): uv sync --all-extras --group=cu130-torch213-train resolves clean, and Cosmos3-Nano throughput inference over all 13 inputs/omni samples (t2i/t2v/i2v/v2v/t2vs/i2vs + 6 action modes) produces valid, non-degenerate video/image/audio/action outputs. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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The x86_64 pin was still on 0.21.6.dev6+cu130.torch210.gb300, which is built against the torch 2.10 ABI: on torch 2.13 the extension fails to load and natten.HAS_LIBNATTEN silently reports False. nvidia-cosmos/cosmos-dependencies#62 published the matching x86_64 wheel, so both arches can now share a single unmarked pin. Verified on 4x H200: full Cosmos3-Nano omni suite (13/13 samples, all 7 model_modes) with HAS_LIBNATTEN=True. NATTEN is the active attention backend here — flash3 is absent and cuDNN 9.20 is below the 9.22 the framework wants, so choose_backend resolves to natten. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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What
Adds
cu130-torch213andcu130-torch213-traindependency groups pinning torch 2.13.0+cu130 (with matching torchvision 0.28.0, torchcodec 0.14.0, torchao 0.17.0, triton 3.7.1,natten 0.21.6+cu130.torch213on aarch64, and the cu13 NVIDIA runtime libs), plus aconflictsentry making it mutually exclusive with the other backend groups.uv.lockis regenerated for the new closure.Why the code change
torchvision 0.28 removed
torchvision.io.read_video, socosmos_framework/inference/vision.pyswitches to a torchcodec-based THWC uint8 decoder. It's forced onto CPU so an active default-CUDA context during generation doesn't route torchcodec's internal frame-index tensor to CUDA (which would raiseNotImplementedError).Docs
Bumps the recommended NGC base image to
nvcr.io/nvidia/pytorch:26.06-py3inREADME.mdanddocs/setup.mdto match the torch 2.13 line.Verification
On 4× GB200 (aarch64):
uv sync --all-extras --group=cu130-torch213-trainresolves clean;torch.__version__ == 2.13.0+cu130,torch.version.cuda == 13.0.throughputinference over all 13inputs/omni/*.jsonsamples (t2i, t2v, i2v, v2v, t2vs, i2vs, and the 6 action modes) produced valid outputs: videos decode with real pixel variation (std 44–76, well above the degenerate floor), audio modes carry an audio track, and all action arrays are non-empty and finite.Known limitations (in code comments)
🤖 Generated with Claude Code