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/benchmark_native_replay.py

@@ -0,0 +1,104 @@
+"""Compare resident native modules with exact replay and paired batch timings.
+
+Fixtures can contain private weights; this utility never packages them.
+"""
+import argparse
+import hashlib
+import json
+import os
+import pathlib
+import subprocess
+import sys
+
+from profile_report import report
+
+
+def sha(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def run(args):
+    args.output_dir.mkdir(parents=True, exist_ok=False)
+    summary = dict(passed=False, numericalExact=False, fixture=str(args.fixture_dir),
+                   module=str(args.module), candidate=str(args.candidate), probe=str(args.probe),
+                   pairs=args.pairs, batches=args.batches,
+                   timingSources=args.timings, executions=[])
+    environment = os.environ.copy()
+    environment['D4R_DIAG_DIR'] = str(args.output_dir)
+    environment['PATH'] = str(args.hip_root / 'bin') + os.pathsep + environment.get('PATH', '')
+    try:
+        summary.update(moduleSha256=sha(args.module), candidateSha256=sha(args.candidate),
+                       probeSha256=sha(args.probe))
+        def execute(name, module, options=()):
+            command = [str(args.probe), '--hip-root', str(args.hip_root), '--module', str(module),
+                       '--fixture-dir', str(args.fixture_dir), '--output-dir', str(args.output_dir / name),
+                       '--iterations', '1', *options]
+            with (args.output_dir / (name + '.stdout.log')).open('wb') as out, \
+                    (args.output_dir / (name + '.stderr.log')).open('wb') as err:
+                try:
+                    completed = subprocess.run(command, stdout=out, stderr=err, env=environment, timeout=180)
+                except subprocess.TimeoutExpired:
+                    summary['executions'].append(dict(name=name, command=command, timedOut=True))
+                    raise
+            summary['executions'].append(dict(name=name, command=command, exitCode=completed.returncode))
+            if completed.returncode:
+                raise RuntimeError(f'{name} failed ({completed.returncode}); inspect preserved logs/dump')
+            return args.output_dir / name
+
+        control = execute('control-reference', args.module)
+        candidate = execute('candidate-reference', args.candidate)
+        reference = {p.name: sha(p) for p in control.glob('alloc-*.bin')}
+        if not reference:
+            raise RuntimeError('Replay produced no allocations')
+
+        def check(folder):
+            actual = {p.name: sha(p) for p in folder.glob('alloc-*.bin')}
+            if actual != reference:
+                changed = sorted(set(actual) ^ set(reference) |
+                                 {name for name in reference if actual.get(name) != reference[name]})
+                raise RuntimeError(f'{folder.name}: allocations differ from fresh control: {changed}')
+
+        # Stop before timing a candidate that changes the numerical result.
+        check(candidate)
+        summary['numericalExact'] = True
+        summary['allocationSha256'] = reference
+        for timing in args.timings:
+            for batch in args.batches:
+                folder = execute(f'{timing}-{batch}', args.module,
+                                 ['--benchmark-module', str(args.candidate), '--benchmark-timing', timing,
+                                  '--benchmark-batch', str(batch), '--iterations', str(args.pairs)])
+                check(folder)
+        measured = report(args.output_dir)
+        summary['profile'] = measured
+        expected = len(args.timings) * len(args.batches)
+        if len(measured['replayPairs']) != expected or any(
+                row['controlGpuMs']['samples'] != args.pairs for row in measured['replayPairs']):
+            raise RuntimeError('Incomplete/invalid timing pairs; this run is not accepted for performance comparison')
+        summary['passed'] = True
+        for row in measured['replayPairs']:
+            print(f"PAIRED {row['kernel']} {row['timingSource']} batch={row['launchesPerSample']} "
+                  f"control={row['controlGpuMs']['median']:.6f} candidate={row['candidateGpuMs']['median']:.6f} ms "
+                  f"ratio={row['candidateToControl']['median']:.6f}")
+        print('PASS NATIVE_BENCHMARK exact_allocations=1 timings_valid=1 '
+              'isolated_replay=1 game_speedup=unmeasured')
+    except Exception as error:
+        summary['error'] = str(error)
+        print(f'FAIL NATIVE_BENCHMARK {error}', file=sys.stderr)
+    finally:
+        (args.output_dir / 'summary.json').write_text(json.dumps(summary, indent=2) + '\n', encoding='utf-8')
+    return 0 if summary['passed'] else 5
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description=__doc__)
+    for name in ('probe', 'hip-root', 'module', 'candidate', 'fixture-dir', 'output-dir'):
+        parser.add_argument('--' + name, required=True, type=lambda s: pathlib.Path(s).resolve())
+    parser.add_argument('--pairs', type=int, default=32)
+    parser.add_argument('--batches', type=int, nargs='+', default=[1, 64])
+    parser.add_argument('--timings', choices=['events', 'dispatch'], nargs='+', default=['events', 'dispatch'])
+    args = parser.parse_args()
+    if not 1 <= args.pairs <= 10000 or any(not 1 <= batch <= 256 for batch in args.batches):
+        parser.error('pairs must be 1..10000 and batches 1..256')
+    if len(set(args.batches)) != len(args.batches) or len(set(args.timings)) != len(args.timings):
+        parser.error('duplicate batches/timing sources are not allowed')
+    raise SystemExit(run(args))