feat: add d4r inference engines and Windows parity tooling

Add native K/M/RR inference engines, HIP kernels, replay and validation
harnesses, and Windows DLSS setup and runtime support. Include GPU
profiling tools, build and packaging scripts, platform integration
patches, and implementation documentation.

tools/windows/test_ptx_reference.py

@@ -0,0 +1,46 @@
+"""Regression for divergent finite loops in the public CPU PTX reference."""
+import pathlib
+import sys
+import tempfile
+import unittest
+
+import numpy as np
+
+ROOT = pathlib.Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(ROOT / 'kernels/tools'))
+import ptxsim
+
+
+class ControlFlow(unittest.TestCase):
+    def test_divergent_backward_loop_reconverges(self):
+        source = '''.visible .entry pattern(.param .align 8 .b8 pattern_param_0[8])
+        .maxntid 32, 1, 1
+        {
+        .reg .b32 %r<4>;
+        .reg .pred %p<1>;
+        mov.u32 %r0, %tid.x;
+        and.b32 %r0, %r0, 3;
+        add.u32 %r0, %r0, 1;
+        mov.u32 %r1, 0;
+        $loop:
+        add.u32 %r1, %r1, %r0;
+        sub.u32 %r0, %r0, 1;
+        setp.ne.u32 %p0, %r0, 0;
+        @%p0 bra $loop;
+        add.u32 %r2, %r1, 7;
+        ret;
+        }
+        '''
+        with tempfile.TemporaryDirectory() as directory:
+            path = pathlib.Path(directory) / 'pattern.ptx'
+            path.write_text(source)
+            statements, types, shared = ptxsim.parse_kernel(str(path), 'pattern')
+            block = ptxsim.Block(statements, types, shared, None, 'pattern_param_0', (32, 1, 1), (1, 1, 1), (0, 0, 0))
+            block.run(limit=1000)
+        n = np.arange(32) % 4 + 1
+        np.testing.assert_array_equal(block.regs['%r1'], n * (n + 1) // 2)
+        np.testing.assert_array_equal(block.regs['%r2'], n * (n + 1) // 2 + 7)
+
+
+if __name__ == '__main__':
+    unittest.main()