Add RDNA4 (gfx12) support and a 0.1.2 optimization round

RDNA4 (experimental; untested on RDNA4 hardware)
- kernels/common/wmma_layout.h picks the WMMA operand/accumulator layout
  per target; K (pwin_*) and M (swin_*) index through it, so the same
  sources build for gfx11 and gfx12. D4R_WMMA_LAYOUT=12 on gfx11 is a test
  shim that runs the gfx12 indexing on gfx11 WMMA.

- M can use native e4m3 FP8 WMMA on gfx12 (D4R_NATIVE_FP8=1). Texture
  tails (enc0_tail, dec0_head) get gfx12 f16 and FP8 paths that match
  ZLUDA's gfx12 lowering.

- ZLUDA patch 0007: gfx12 WMMA helpers, optional native FP8 MMA
  (D4R_ZLUDA_WMMA_FP8_NATIVE), a layout override for testing, and an
  architecture argument for d4r_emit. Texture kernels are now compiled
  offline per target through d4r_emit (D4R_ZLUDA_EMIT) instead of on the
  local GPU.

- New d4r.ini option [Kernels] NativeFp8 (default on), wired through the
  shim and d4r_config.py. On gfx12 the bridge serves kernels/<arch>-fp8
  when it is on.

- The bridge picks the KFD GPU with the most SIMDs (so an iGPU is not
  chosen over a discrete GPU); D4R_GPU_ARCH overrides it.

- kernels/build.sh and package_release.sh build gfx1100-gfx1103 and
  gfx1200/gfx1201 (plus -fp8 variants), and D4R_BUNDLE_TEX takes
  per-target folders.

Performance (RDNA3; harness byte-identical unless noted)
- ZLUDA patch 0006: m16n8k8 f16 MMAs on WMMA (E; not bit-exact, 52-55 dB
  per layer, D4R_ZLUDA_WMMA_K8=0 disables it), per-key weight-image slots
  for native prep kernels (d4r_prep_key_at, d4r_prep_key_slots), and
  wave64 builds of MMA-free kernels (D4R_ZLUDA_WAVE64).

- M: packed FP8 encoding, conflict-free patch-merge reads, scalar weight
  loads in enc1, and weight prep once per weight set for the six enc3
  tube blocks.

- K: CU mode for the six non-wide pwin layers.

- Texture kernels: every DLSS flag variant is built natively (24 K output,
  8 M post, 4 M downsample, 4 M enc0), not only Townfall's; post and K
  output run as wave64.

- M direct output: rrlite_downsample_kernel writes the shim's output
  buffer; the shim's default direct-output presets are now 11,13.

Tools
- dump_runner takes the prep grid from d4r_prep_blocks and refuses
  texture/surface dumps.

- New pwin_check.py, swin_check.py, model_enc3.py and tail_check.hip.

Docs: README GPU table and RDNA4 notes, building (patches 0002-0007,
d4r_emit), native-kernels (RDNA4 section), performance (new numbers),
packaging README. Version 0.1.2.

kernels/tools/pwin_check.py

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