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/swin_check.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""Compares native preset-M Swin-block outputs with the numpy model (swin_model.py) on sampled blocks of a dump.
+
+usage: swin_check.py DUMP_DIR OUT_DIR [OUT_DIR...] [--blocks N]
+  DUMP_DIR  replay dump of one rrlite_{enc1,enc2,enc3_tube,dec1,dec2}_4x4 launch
+  OUT_DIR   dump_runner output of a native kernel on that dump
+The stored e4m3 outputs (block output, and the patch merge of encoders that have one) are compared code by code:
+the share of identical codes, of codes one e4m3 step apart, and the PSNR of the decoded values. The model is
+NVIDIA's arithmetic (e4m3 activations, f16 accumulation per k32 step); the production kernels keep activations
+f16 and accumulate in f32 (SWIN_NO_Q8, SWIN_F32ACC), so they are not expected to match it exactly.
+"""
+import os
+import random
+import sys
+
+import numpy as np
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+import model_enc3 as me  # noqa: E402
+import swin_model as sm  # noqa: E402
+
+# kernel -> (C, heads, NPM, CIN)
+LAYERS = {'enc1': (64, 2, 96, 0), 'enc2': (96, 4, 128, 0), 'enc3_tube': (128, 4, 0, 0), 'dec2': (96, 4, 0, 128),
+          'dec1': (64, 2, 0, 96)}
+
+
+def blocks(grid, n, seed=1):
+    gx, gy = grid[0], grid[1]
+    b = {(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(b) < min(n, gx * gy):
+        b.add((r.randrange(gx), r.randrange(gy)))
+    return sorted(b)
+
+
+def model_block(P, layer, bx, by):
+    C, heads, NPM, CIN = LAYERS[layer]
+    pre = 4 * C * CIN + 8 * C if CIN else 0
+    x0 = sm.decoder_input(P, bx, by, C, CIN, heads, P.p32) if CIN else sm.load_tokens(P, bx, by, C // 32)
+    x2, out, L = sm.swin_block(P, x0, C, heads, pre)
+    merged = None
+    if NPM:
+        A = sm.patch_merge_input(out, C)
+        PM0 = L['end']
+        cols = []
+        for g in range(NPM // 32):
+            Wg = me.E4M3[P.wbuf[sm.woff_table(PM0 + g * 128 * C, 512, 64 * C, 4 * C, 32)]]
+            bias = P.f16vec(PM0 + 4 * C * NPM + 64 * g, 32)
+            cols.append(me.mma_chain(A, Wg, C0=np.broadcast_to(bias, (16, 32)).copy()))
+        merged = me.q8(sm.pair_to_nat(np.concatenate(cols, axis=1)))
+    return out, merged
+
+
+def codes_at(buf, base, ptr, W, H, X, Y, C):
+    """e4m3 codes of tokens (X, Y) (in bounds) from planes of 32 channels at ptr"""
+    c = np.arange(C)
+    off = ptr - base + (((c[None, :] >> 5) * H + Y[:, None]) * W + X[:, None]) * 32 + (c[None, :] & 31)
+    return buf[off]
+
+
+def main():
+    args = [a for a in sys.argv[1:] if not a.startswith('--')]
+    n = 8
+    if '--blocks' in sys.argv:
+        n = int(sys.argv[sys.argv.index('--blocks') + 1])
+        args.remove(str(n))
+    dump, outs = args[0], args[1:]
+    P = me.Params(dump)
+    layer = P.kernel.replace('rrlite_', '').replace('_4x4', '')
+    C, heads, NPM, CIN = LAYERS[layer]
+    bl = blocks(P.grid, n)
+    models = {b: model_block(P, layer, *b) for b in bl}
+    for out_dir in outs:
+        got, want = [], []
+        for (bx, by) in bl:
+            base, _ = P.alloc_of(P.out)
+            aid = next(i for i, (b0, buf) in P.allocs.items() if b0 == base)
+            buf = np.fromfile(f'{out_dir}/alloc-{aid}.bin', np.uint8)
+            X, Y = P.token_xy(bx, by)
+            ok = (X >= 0) & (X < P.W) & (Y >= 0) & (Y < P.H)
+            out, merged = models[(bx, by)]
+            got.append(codes_at(buf, base, P.out, P.W, P.H, X[ok], Y[ok], C).ravel())
+            want.append(out[ok].ravel())
+            if NPM:
+                W2, H2 = P.W // 2, P.H // 2
+                r = np.arange(16)
+                MX, MY = (8 * bx - P.sx) // 2 + (r & 3), (8 * by - P.sy) // 2 + (r >> 2)
+                okm = (MX >= 0) & (MX < W2) & (MY >= 0) & (MY < H2)
+                mbase, _ = P.alloc_of(P.p48)
+                mid = next(i for i, (b0, _) in P.allocs.items() if b0 == mbase)
+                mbuf = buf if mid == aid else np.fromfile(f'{out_dir}/alloc-{mid}.bin', np.uint8)
+                got.append(codes_at(mbuf, mbase, P.p48, W2, H2, MX[okm], MY[okm], NPM).ravel())
+                want.append(merged[okm].ravel())
+        g = me.E4M3[np.concatenate(got)]
+        w = np.concatenate(want).astype(np.float64)
+        rank = lambda v: np.searchsorted(me._POS, np.abs(v)) * np.sign(v)  # noqa: E731
+        steps = np.abs(rank(g) - rank(w))
+        psnr = 10 * np.log10(np.nanmax(np.abs(w)) ** 2 / max(np.nanmean((g - w) ** 2), 1e-30))
+        print(f'{layer:9s} {os.path.basename(os.path.normpath(out_dir)):10s} {len(bl)} blocks {g.size} codes: '
+              f'identical {100 * (g == w).mean():5.1f}%  <=1 step {100 * (steps <= 1).mean():6.2f}%  '
+              f'max {int(steps.max())} steps  psnr {psnr:.1f} dB')
+
+
+if __name__ == '__main__':
+    main()