Clear windows branch; preserve previous contents in win-old
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@@ -1,127 +0,0 @@
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#!/usr/bin/env python3
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"""Compares native preset-K layer outputs with the numpy model (pwin_model.py) on sampled windows of a replay dump.
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usage: pwin_check.py DUMP_DIR OUT_DIR [OUT_DIR...] [--windows N]
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DUMP_DIR replay dump of one dltss_pwin_* launch (inputs)
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OUT_DIR dump_runner output of a native kernel on that dump (alloc-N.bin after the launch)
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For every sampled window (the four corners, border windows and random interior ones) the model output and the
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kernel's stored values are compared per f16 element: exact matches, and the error in f16 ulps at the model
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value's magnitude (values below 2^-10 count with the ulp of 2^-10, so near-zero sign flips do not dominate).
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The kernels round as NVIDIA's f16 wmma does except in the PWIN_F32ACC layers, and gfx12 WMMA groups its f32
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sums differently from gfx11, so neither build is expected to match the model exactly.
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"""
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import os
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import random
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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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import pwin_model as pm # noqa: E402
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F16 = np.float16
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# layer -> (kind, H, C, COUT/CL, extra)
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LAYERS = {
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'enc0': ('enc0', 2, 32, 64, None), 'enc1': ('enc', 2, 64, 64, True), 'enc2': ('enc', 2, 64, 96, False),
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'enc3': ('enc', 4, 96, 128, False), 'enc4': ('enc', 4, 128, 160, False), 'dec5': ('plain', 8, 160, 16, None),
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'dec4': ('dec', 4, 128, 160, None), 'dec3': ('dec', 4, 96, 128, None), 'dec2': ('dec', 2, 64, 96, None),
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'dec1': ('dec', 2, 64, 64, None), 'dec0': ('dec0', 2, 32, 64, None),
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}
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class Outputs:
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"""the dump's pointer map over the allocations a native run saved"""
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def __init__(self, dump, out_dir):
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self.d = dump
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self.allocs = {i: (base, np.fromfile(f'{out_dir}/alloc-{i}.bin', np.uint8)) for i, (base, _) in dump.allocs.items()}
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def rows(self, off_param, index, width):
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a = self.d.u64(off_param)
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for base, buf in self.allocs.values():
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if base <= a < base + buf.size:
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o = a - base + 2 * index * width
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return buf[o:o + 2 * width].view(F16)
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raise ValueError(hex(a))
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def ulps(model, got):
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m = model.astype(np.float64)
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g = got.astype(np.float64)
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scale = np.maximum(np.abs(m), 2.0 ** -10)
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ulp = 2.0 ** (np.floor(np.log2(scale)) - 10)
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return np.abs(m - g) / ulp
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def windows(grid, n, seed=1):
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gx, gy = grid[0], grid[1]
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w = {(0, 0), (gx - 1, 0), (0, gy - 1), (gx - 1, gy - 1), (gx // 2, 0), (0, gy // 2), (gx - 1, gy // 2), (gx // 2, gy - 1)}
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r = random.Random(seed)
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while len(w) < min(n, gx * gy):
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w.add((r.randrange(gx), r.randrange(gy)))
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return sorted(w)
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def model_window(P, layer, bx, by):
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kind, H, C, X, extra = LAYERS[layer]
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if kind == 'enc0':
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return pm.pwin_enc0(P, bx, by)
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if kind == 'enc':
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return pm.pwin_encoder(P, bx, by, H, C, X, posattn=extra)
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if kind == 'plain':
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return pm.swin_core(P, pm.Layout(H, C, C), pm.load_window(P, bx, by, C)), None
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if kind == 'dec':
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return pm.pwin_decoder(P, bx, by, H, C, X), None
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return pm.pwin_dec0(P, bx, by)
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def stored(P, O, layer, bx, by, y, extra_out):
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"""(model values, kernel values) of everything the kernel stores for window (bx, by)"""
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kind, H, C, X, _ = LAYERS[layer]
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W, Hh = P.i32x2(0)
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sx, sy = P.i32x2(56)
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ms, gs = [], []
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full_off = 32 if kind in ('enc0', 'enc') else 24
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for t in range(64):
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Y0, X0 = 8 * by - sy + (t >> 3), 8 * bx - sx + (t & 7)
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if not (0 <= Y0 < Hh and 0 <= X0 < W):
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continue
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if kind == 'dec0':
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ms.append(extra_out[t]); gs.append(O.rows(24, Y0 * W + X0, 40))
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else:
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ms.append(y[t]); gs.append(O.rows(full_off, Y0 * W + X0, C))
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if kind in ('enc0', 'enc'):
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cout = extra_out.shape[1]
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for r in range(16):
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Ym, Xm = int((8 * by - sy) / 2) + (r >> 2), int((8 * bx - sx) / 2) + (r & 3)
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if 0 <= Ym < Hh // 2 and 0 <= Xm < W // 2:
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ms.append(extra_out[r]); gs.append(O.rows(24, Ym * (W // 2) + Xm, cout))
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return np.concatenate(ms), np.concatenate(gs)
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def main():
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args = [a for a in sys.argv[1:] if not a.startswith('--')]
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n = 12
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if '--windows' in sys.argv:
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n = int(sys.argv[sys.argv.index('--windows') + 1])
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args.remove(str(n))
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dump_dir, outs = args[0], args[1:]
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P = pm.Dump(dump_dir)
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layer = P.kernel.replace('dltss_pwin_', '').replace('_layer', '')
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wins = windows(P.grid, n)
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models = {w: model_window(P, layer, *w) for w in wins}
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for out in outs:
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O = Outputs(P, out)
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m, g = zip(*(stored(P, O, layer, bx, by, *models[(bx, by)]) for bx, by in wins))
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m, g = np.concatenate(m), np.concatenate(g)
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u = ulps(m, g)
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exact = (m.view(np.uint16) == g.view(np.uint16)).mean()
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md, gd = m.astype(np.float64), g.astype(np.float64)
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psnr = 10 * np.log10(np.abs(md).max() ** 2 / max(((md - gd) ** 2).mean(), 1e-30))
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print(f'{layer:5s} {os.path.basename(os.path.normpath(out)):10s} {len(wins)} windows {m.size} values: exact {100 * exact:5.1f}% '
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f'<=1ulp {100 * (u <= 1).mean():5.1f}% <=4ulp {100 * (u <= 4).mean():6.2f}% max {u.max():7.1f} ulp '
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f'mean {u.mean():.3f} ulp psnr {psnr:.1f} dB finite {np.isfinite(g.astype(np.float32)).mean() * 100:.0f}%')
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if __name__ == '__main__':
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main()
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