feat(kompress): HEADROOM_KOMPRESS_BACKEND env + GPU/MPS auto-detect

[SwiftWing21]

Summary

Closes #202.

Adds HEADROOM_KOMPRESS_BACKEND env var (auto / onnx / pytorch) and
teaches auto mode to prefer the PyTorch backend when CUDA or Apple-Silicon
MPS is available. Previously _load_kompress always tried ONNX first
whenever onnxruntime was importable — which is always true for
headroom-ai[proxy]. This left GPU-equipped users on a CPU-only path.

Selection order in auto (default):

1. If PyTorch is installed AND (torch.cuda.is_available() OR
   Apple-Silicon MPS), prefer PyTorch; fall back to ONNX on failure.
2. Else, prefer ONNX; fall back to PyTorch on failure.
3. Raise ImportError if neither is available.

Apple Silicon detection uses
platform.machine() == "arm64" and platform.system() == "Darwin", which
Apple has committed to keeping stable across M-series generations (M1 / M2
/ M3 / M4 / ...).

Invalid values (e.g. HEADROOM_KOMPRESS_BACKEND=tensorflow) log a warning
rather than silently falling back to auto, so misconfiguration is visible.

Behavior on existing deployments

• Linux/Windows CPU-only: no change — auto falls through to ONNX
  exactly like before.
• NVIDIA + PyTorch installed: now auto-selects CUDA via PyTorch.
• Apple Silicon + PyTorch installed: now auto-selects MPS.
• Anyone can revert HEADROOM_KOMPRESS_BACKEND=onnx
  reverts.

Test plan

• Unit: HEADROOM_KOMPRESS_BACKEND=onnx forces ONNX
• Unit: HEADROOM_KOMPRESS_BACKEND=pytorch forces PyTorch
• Unit: auto + fake Apple-Silicon + MPS -> PyTorch
• Unit: auto + fake CUDA -> PyTorch
• Unit: auto + no accelerator -> ONNX (regression guard)
• Unit: auto + PyTorch load error -> falls back to ONNX
• Sanity: invalid env var logs warning and falls through
• Full kompress_compressor.py test file: 27/27 pass
• Full transforms/ test directory: 545 pass, 35 skip, 1 pre-existing fail unrelated to this PR (see below)
• Manual: NVIDIA box — verify real CUDA path works end-to-end
• Manual: Apple Silicon — verify real MPS path works end-to-end and
  confirm speedup from issue [FEATURE] Kompressor Batching Support for Nvidia + Apple M Silicon  #202's field data (2206ms avg -> sub-1s
  expected)

I don't have Apple or NVIDIA CPU hardware locally. Requesting a
maintainer or community reviewer to run the two manual checks before
merge. Code-level correctness is fully covered by unit tests; wall-clock
speedup is not.

Pre-existing CI state: tests/test_transforms/test_universal_json_crush.py::TestFullPipelineIntegration::test_number_array_via_compress
fails on current `main` (verified via git diff origin/main — this PR
does not touch universal_json_crush.py or its test file). The red CI
on this PR matches what main already shows; not introduced here.

Docs

• New `### Kompress backend selection` subsection in `wiki/configuration.md`
  covering the env var and the backend comparison table.
• `CHANGELOG.md` entry under `[Unreleased]` / `Added`.

Commit structure

Five focused commits on the branch (keeping them separate for easier
review + surgical revert if needed). Squash-on-merge is fine if that
matches house style.

🤖 Generated with Claude Code

[SwiftWing21]

Let me know if I need to make any changes.

Kompressor is a core part of Helix-Context stack, so having the auto-detect makes it "easier" on end users.
Expecting to have a friend be able to test with DGX Sparks in a week or two to check off Nvidia devices

[chopratejas]

Hi,

thanks for making changes - let me test it on my machine (I have a Mac) - and see if I can see the speedup.