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.
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#!/usr/bin/env python3
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"""Pack a denoiser stage's weights (engine/rr/enc1_ref.py --extract) for the native stage shaders.
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usage: native_pack.py WEIGHTS.npz OUT.bin OUT.h
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Every FP8 matrix W (K x N, rows in NVIDIA's k order) becomes 16x16 tiles, tile (kt, nt) at
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256 * (kt * N/16 + nt) bytes, row-major inside the tile (stride 16): a B operand of the cooperative matrix loads.
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Every f16 matrix is stored as is (row-major); an f16 bias vector (a 1-D entry whose name starts with 'b') as 16
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equal rows, so that it loads as an accumulator tile. OUT.h lists the byte offset of every entry as a #define,
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for the shader's include.
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"""
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import sys
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import numpy as np
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def tiles(w):
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k, n = w.shape
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assert k % 16 == 0 and n % 16 == 0, w.shape
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return w.reshape(k // 16, 16, n // 16, 16).transpose(0, 2, 1, 3).reshape(-1)
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def main():
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src, out_bin, out_h = sys.argv[1:4]
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w = dict(np.load(src))
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blob = bytearray()
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defines = []
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for name in sorted(w):
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a = w[name]
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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 a.dtype == np.uint8:
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blob += tiles(a).tobytes()
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elif a.ndim == 1 and name.startswith('b'):
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blob += np.tile(np.asarray(a, np.float16), (16, 1)).tobytes()
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else:
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blob += np.asarray(a, np.float16).tobytes()
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defines.append(f'#define W_{name.upper()} {off}u // {a.dtype} {a.shape}')
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open(out_bin, 'wb').write(bytes(blob))
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open(out_h, 'w').write('// generated by engine/rr/native_pack.py\n' + '\n'.join(defines) + '\n')
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print(f'{len(blob)} bytes, {len(w)} entries')
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if __name__ == '__main__':
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main()
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