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.
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Pack a Swin stage's extracted weights (engine/rr/swin_ref.py --extract) for engine/rr/swin_m.comp.
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usage: swin_pack.py STAGE WEIGHTS.npz OUT.bin OUT.h
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enc1_pack.py's layout for any stage of engine/rr/swin_ref.STAGES: transposed products, every weight's columns in the
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k order of the product that consumes its result (slot j holds natural channel inter(n)[j]); the expand's 4C columns
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are four sub-positions of C channels, each permuted the same way. OUT.h also defines the stage's shape for the shader
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(KIND, C, CX, NH).
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"""
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import os
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import sys
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from enc1_ref import inter # noqa: E402
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from enc1_pack import a_tiles, b_tiles, cubic_table, act_table # noqa: E402
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from swin_ref import STAGES # noqa: E402
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def main():
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stage, src, out_bin, out_h = sys.argv[1:5]
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kind, C, CX, NH, _ = STAGES[stage]
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w = dict(np.load(src))
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I32, I96, IC = inter(32), inter(96), inter(C)
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e = {}
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if kind == 'dec':
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I4C = [C * (j // C) + IC[j % C] for j in range(4 * C)]
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e['we'], e['be'] = ('A', w['we'][:, I4C]), ('H', w['be'][I4C])
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for h in range(NH):
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e[f'wk{h}'] = ('A', w[f'wk{h}'][:, I32])
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e[f'wv{h}'] = ('B', w[f'wv{h}'][:, I32])
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e[f'wo{h}'] = ('A', w[f'wo{h}'][:, IC])
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e[f'bias{h}'] = ('H', w[f'bias{h}'][np.ix_(inter(64), I96)].T)
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for b in range(C // 8):
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e[f'w1_{b}'] = ('A', w[f'w1_{b}'][:, I32])
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e[f'b1_{b}'] = ('H', w[f'b1_{b}'][I32])
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e[f'w2_{b}'] = ('A', w[f'w2_{b}'][:, IC])
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if kind == 'enc' and CX:
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e['wm'], e['bm'] = ('A', w['wm'][:, inter(CX)]), ('H', w['bm'][inter(CX)])
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e['wh'], e['bh'] = ('A', w['wh']), ('H', w['bh'])
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e['gain0'] = ('H', w['gain0'])
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for k in ('table7', 'gain14', 'table16'):
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e[k] = ('H', w[k][IC])
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e['cubic'] = ('U', cubic_table())
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e['act'] = ('Y', act_table())
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blob, defines = bytearray(), []
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for name, (kd, a) in e.items():
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while len(blob) % 256:
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blob.append(0)
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off = len(blob)
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if kd == 'A':
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blob += a_tiles(a).tobytes()
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elif kd == 'B':
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blob += b_tiles(a).tobytes()
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elif kd == 'Y':
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blob += np.ascontiguousarray(a, np.uint8).tobytes()
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elif kd == 'U':
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blob += np.ascontiguousarray(a, np.uint16).tobytes()
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else:
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blob += np.ascontiguousarray(a, np.float16).tobytes()
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defines.append(f'#define W_{name.upper()} {off}u // {kd} {a.shape}')
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shape = [f'#define KIND {0 if kind == "enc" else 1}', f'#define C {C}', f'#define CX {CX}', f'#define NH {NH}']
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if NH < 2:
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defines.append('#define W_WK1 (W_WK0 + 1u)')
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if C // 8 < 2:
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defines.append('#define W_W1_1 (W_W1_0 + 1u)')
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open(out_bin, 'wb').write(bytes(blob))
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open(out_h, 'w').write(f'// generated by engine/rr/swin_pack.py for {stage}\n' + '\n'.join(shape + defines) + '\n')
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print(f'{stage}: {len(blob)} bytes, {len(e)} entries')
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if __name__ == '__main__':
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main()
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