switch from platform markers for torch to uv extras for more flexible install

[rbavery]

This PR implements uv extras for separating cpu from cuda dependencies instead of platform restrictions. It allows for installing cpu only dependencies on linux, which is useful in CI and for depending on olmo_earth in projects that only install cpu dependencies.

I've tested this out how to use olmoearth_pretrain as a dependency in another project.

---

Note

Switches dependency management to uv extras for selecting CPU vs CUDA torch/torchvision, updates README install instructions, and configures uv sources/conflicts with minor dependency tweaks.

• Installation:
  • Update README to show uv sync with --extra torch-cu128 or --extra torch-cpu.
• Dependency/packaging:
  • Introduce uv extras torch-cpu and torch-cu128 with corresponding torch, torchvision, pytorch-triton, and flash-attn wiring.
  • Add [tool.uv] config: extras conflict guard and custom indexes/sources for torch, torchvision, and pytorch-triton.
  • Pin markupsafe<=3.0.0 and adjust metadata accordingly.
  • Move flash-attn handling to tool.uv.extra-build-*.
  • No code changes; lockfile updated to reflect new extras and indexes.

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[Hgherzog]

Hi Ryan, I think making the torch installation explicit rather than platform dependent is very sensible. I will need to test it on our internal cluster as well before we can merge this. We should hold off on the torch version bump for now and just address the platform dependencies in this PR, as we would want to run more extensive tests on our internal hardware before doing that. Are you looking to use this package to load the model or build off of the pre-training code?

[rbavery]

@Hgherzog Got it I removed the torch version bump.

We're exporting the model as PT2 archives (successor format to TorchScript). In order to export as PT2, the version bump would be helpful as newer versions of torch have been improving the PT2 archive format and export utilities. I can use my fork for this work instead though.