ayo
7c5d3c9157
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.
68 lines
3.4 KiB
Python
68 lines
3.4 KiB
Python
"""Public gfx1201 WMMA arithmetic comparison; does not load DLSS or weights."""
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import argparse
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import json
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import os
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import pathlib
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import struct
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import subprocess
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import numpy as np
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def main():
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p = argparse.ArgumentParser(description=__doc__)
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p.add_argument('--probe', type=pathlib.Path, required=True)
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p.add_argument('--module', type=pathlib.Path, required=True)
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p.add_argument('--hip-root', type=pathlib.Path, required=True)
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p.add_argument('--output-dir', type=pathlib.Path, required=True)
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args = p.parse_args()
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root = args.output_dir.resolve()
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root.mkdir(parents=True, exist_ok=True)
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fixture = root / 'fixture'
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fixture.mkdir(exist_ok=True)
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rng = np.random.default_rng(90701201)
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a = rng.uniform(-1, 1, (64, 16, 16)).astype(np.float16)
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b = rng.uniform(-1, 1, a.shape).astype(np.float16)
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c = rng.uniform(-.25, .25, a.shape).astype(np.float16).astype(np.float32)
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initial_output = np.full((64, 3, 16, 16), np.nan, np.float32)
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arrays = (a, b, c, initial_output)
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bases = [0x10000000 * (i + 1) for i in range(4)]
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(fixture / 'args.bin').write_bytes(struct.pack('<4Q', *bases))
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lines = ['kernel d4r_wmma_f16_compare', 'launch 64 1 1 32 1 1 0', 'args 32']
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for i, data in enumerate(arrays):
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data.tofile(fixture / f'alloc-{i}.bin')
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lines += [f'alloc {i} {bases[i]:#x} {data.nbytes}', f'pointer {i * 8} {i} 0']
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(fixture / 'manifest.txt').write_text('\n'.join(lines) + '\n', encoding='utf-8')
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env = os.environ.copy()
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env['D4R_DIAG_DIR'] = str(root)
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with (root / 'gpu.stdout.log').open('wb') as out, (root / 'gpu.stderr.log').open('wb') as err:
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status = subprocess.run([str(args.probe.resolve()), '--hip-root', str(args.hip_root.resolve()),
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'--module', str(args.module.resolve()), '--fixture-dir', str(fixture), '--output-dir',
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str(root / 'output'), '--iterations', '1'], env=env, stdout=out, stderr=err, timeout=120)
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if status.returncode:
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raise RuntimeError(f'GPU probe exit {status.returncode}; see {root}')
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output = np.fromfile(root / 'output/alloc-3.bin', np.float32).reshape(64, 3, 16, 16)
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if not np.isfinite(output).all():
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raise RuntimeError('Nonfinite/unwritten WMMA output')
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rows = []
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for group, steps in enumerate((1, 2, 4, 8)):
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values = output[group * 16:(group + 1) * 16]
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difference = np.abs(values[:, 0].astype(np.float64) - values[:, 1].astype(np.float64))
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row = dict(steps=steps, elements=difference.size, differing=int(np.count_nonzero(difference)),
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maxAbsoluteError=float(difference.max()), rms=float(np.sqrt(np.mean(difference ** 2))))
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rows.append(row)
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print('WMMA_F16_COMPARE ' + json.dumps(row), flush=True)
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adapter_exact = np.array_equal(output[:, 1].view(np.uint32), output[:, 2].view(np.uint32))
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if not adapter_exact:
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raise RuntimeError('Direct packed instruction and layout adapter differ')
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result = dict(passed=True, architecture='gfx1201', publicSynthetic=True, adapterExact=adapter_exact,
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baselineEquivalent=all(row['differing'] == 0 for row in rows), cases=rows,
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gameOptimizationAccepted=False)
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(root / 'validation.json').write_text(json.dumps(result, indent=2) + '\n', encoding='utf-8')
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print('PASS WMMA_F16_ARITHMETIC_DIAGNOSTIC adapter_exact=1 baseline_equivalent=' +
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str(int(result['baselineEquivalent'])), flush=True)
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if __name__ == '__main__':
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main()
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