feat: add Jetson contributor validation path

tests/conditional/test_jetson_runtime.py

@@ -0,0 +1,53 @@
+"""Physical NVIDIA Jetson CUDA placement and operation probe."""
+
+from __future__ import annotations
+
+import platform
+from pathlib import Path
+
+import pytest
+import torch
+import torch.nn as nn
+from accelerate.hooks import AlignDevicesHook, add_hook_to_module
+
+from obliteratus import device
+from obliteratus.abliterate import AbliterationPipeline
+
+
+pytestmark = pytest.mark.gpu
+
+
+def test_jetson_cuda_runtime_contract():
+    assert platform.machine().lower() in {"aarch64", "arm64"}
+    assert Path("/etc/nv_tegra_release").is_file()
+    assert torch.version.cuda is not None
+    assert torch.cuda.is_available()
+    assert torch.cuda.device_count() > 0
+    assert device.is_cuda()
+    assert device.get_device("auto") == "cuda"
+    tensor = torch.arange(16, device="cuda", dtype=torch.float32).reshape(4, 4)
+    result = tensor @ tensor.T
+    assert result.device.type == "cuda"
+    assert torch.isfinite(result).all()
+
+
+def test_jetson_cuda_offloaded_surgery_contract():
+    module = nn.Module()
+    module.proj = nn.Linear(4, 4, bias=False)
+    original = module.proj.weight.detach().clone()
+    hook = AlignDevicesHook(execution_device="cuda", offload=True)
+    add_hook_to_module(module.proj, hook)
+
+    count = AbliterationPipeline._project_out_advanced(
+        module,
+        torch.tensor([[1.0], [0.0], [0.0], [0.0]], device="cuda"),
+        ["proj"],
+    )
+    output = module.proj(torch.ones(1, 4, device="cuda"))
+
+    expected = original.clone()
+    expected[:, 0] = 0
+    assert count == 1
+    assert output.device.type == "cuda"
+    assert module.proj.weight.device.type == "meta"
+    torch.testing.assert_close(hook.weights_map["weight"], expected)