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.
70 lines
3.6 KiB
Python
70 lines
3.6 KiB
Python
"""Independent PTX interpreter validation of real M captures; keep outputs private."""
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import argparse
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import pathlib
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import re
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import sys
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import numpy as np
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from m_replay_validate import me, LAYERS, blocks, model_block, stored, metrics
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import ptxsim
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from extract_dlss_ptx import fatbins, ptx_entries
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def validate(args):
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name = f'rrlite_{args.layer}_4x4'
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parameters = me.Params(str(args.capture_dir))
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if parameters.kernel != name:
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raise RuntimeError('Capture kernel does not match requested M layer')
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native = {i: np.fromfile(args.native_dir / f'alloc-{i}.bin', np.uint8) for i in parameters.allocs}
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candidates = []
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coordinates = args.block or blocks(parameters.grid, args.candidate_blocks)
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for bx, by in coordinates:
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if not (0 <= bx < parameters.grid[0] and 0 <= by < parameters.grid[1]):
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raise RuntimeError(f'Block {bx},{by} is outside the capture grid')
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model = model_block(parameters, args.layer, bx, by)
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expected, actual = stored(parameters, args.layer, bx, by, native, model)
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difference = float(np.abs(expected - actual).max())
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candidates.append((difference, bx, by, model, expected, actual))
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selected = sorted(candidates, key=lambda c: (-c[0], c[1], c[2]))[:args.blocks]
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pattern = re.compile(rb'\.entry\s+' + name.encode() + rb'\s*\(')
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sources = [source.rstrip(b'\0') for _, fatbin in fatbins(args.dlss_dll.read_bytes())
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for source in ptx_entries(fatbin) if pattern.search(source)]
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if not sources:
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raise RuntimeError('Local DLSS DLL does not contain this M kernel')
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args.output_dir.mkdir(parents=True, exist_ok=True)
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path = args.output_dir / (name + '.ptx')
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path.write_bytes(sources[-1])
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for _, bx, by, model, numpy_expected, actual in selected:
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_, memory, steps = ptxsim.run_block(str(path), name, str(args.capture_dir), (bx, by, 0))
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allocation_data = dict(zip(memory.ids, memory.data))
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_, expected = stored(parameters, args.layer, bx, by, allocation_data, model)
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print(f'PTX_REFERENCE layer={args.layer} block={bx},{by} statements={steps}', flush=True)
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_, _, psnr, fp8_steps = metrics(expected, actual, 'PTX_VS_NATIVE')
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metrics(expected, numpy_expected, 'PTX_VS_NUMPY')
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if fp8_steps.max() > 1 or psnr < 50:
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raise RuntimeError('Native M differs from independent PTX reference')
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print(f'PASS M_PTX_REFERENCE layer={args.layer} blocks={len(selected)} nan_inf=0', flush=True)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('--layer', choices=list(LAYERS), required=True)
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parser.add_argument('--capture-dir', type=pathlib.Path, required=True)
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parser.add_argument('--native-dir', type=pathlib.Path, required=True)
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parser.add_argument('--dlss-dll', type=pathlib.Path, required=True)
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parser.add_argument('--output-dir', type=pathlib.Path, required=True)
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parser.add_argument('--blocks', type=int, default=2)
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parser.add_argument('--candidate-blocks', type=int, default=12,
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help='NumPy scan size used to choose the worst blocks for independent PTX')
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parser.add_argument('--block', type=lambda value: tuple(map(int, value.split(','))),
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action='append', help='Explicit x,y regression block (repeatable)')
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args = parser.parse_args()
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if args.blocks < 1 or args.candidate_blocks < 1:
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parser.error('--blocks must be positive')
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try:
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validate(args)
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except Exception as error:
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print(f'FAIL M_PTX_REFERENCE {error}', file=sys.stderr)
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raise SystemExit(5)
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