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.
27 lines
1.3 KiB
Python
27 lines
1.3 KiB
Python
"""Compare compile-time Swin diagnostic snapshots with the NumPy intermediate values."""
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import argparse
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import numpy as np
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from m_replay_validate import me, sm, LAYERS, metrics
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument('--capture-dir', required=True)
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parser.add_argument('--stages', required=True)
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parser.add_argument('--block', default='0,4')
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parser.add_argument('--layer', choices=list(LAYERS), default='enc1')
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args = parser.parse_args()
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p = me.Params(args.capture_dir)
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c, heads, _, cin = LAYERS[args.layer]
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bx, by = map(int, args.block.split(','))
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pre = 4 * c * cin + 8 * c if cin else 0
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x0 = sm.decoder_input(p, bx, by, c, cin, heads, p.p32) if cin else sm.load_tokens(p, bx, by, c // 32)
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stages = {}
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sm.swin_block(p, x0, c, heads, pre, stages=stages)
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actual = np.fromfile(args.stages, np.float16).reshape(4, 64, 128).astype(np.float64)
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for index, name in enumerate(('h1', 'x1', 'h2', 'O')):
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expected = stages[name]
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values = actual[index, :, :expected.shape[1]]
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metrics(expected, values, f'SWIN_STAGE {name}')
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different = np.argwhere(expected != values)
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for row, channel in different[:8]:
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print(f'DIFFERENT stage={name} token={row} channel={channel} reference={expected[row, channel]} native={values[row, channel]}')
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