swin_tok.h passes the weights as the first WMMA operand and the activations as the second, so each GEMM's encoded result is already the next one's operand and stays in registers (swin_block.h transposes every result through LDS). One wave owns one 4x4 window for the whole block: norms, attention and the MLP need no LDS and no barriers; only the patch expand and the patch merge go through an e4m3 image in LDS. Outputs are bit-identical to swin_block.h (swapped WMMA operand roles, verified on an RX 9070 XT and on recorded launches). mix2 rounds in place (accumulator is addend and destination, no copy per pair). -DSWIN_NO_TOKEN_LANES builds these layers from swin_block.h again; enc2 and dec2 are unchanged. RX 9070 XT accuracy set, 2560x1440 render: whole evaluation 8.56 -> 7.18 ms.
The 2x figure is against the previous native-FP8 layers. With NativeFp8 off
the RX 9070 XT was already at 3.37 ms for 1280x720 -> 3840x2160; the FP8 set
is 2.88 ms there.
The FP8 Swin layers encode with a clamp to +-448 (NaN-propagating packed
minimum/maximum) followed by v_cvt_pk_fp8_f32, in both the fast and the
accuracy build: equal to half_to_e4m3 for every non-NaN input, NaN encodes
as 0xff. The accuracy build rounds each k32 step with v_fma_mix (K32_MIX).
On gfx12 the accuracy builds of M's post and downsample kernels are wave64
with denormal handling kept.
RX 9070 XT, 1280x720 -> 3840x2160, D3D12 harness: the accuracy set goes
from 3.54 ms to 3.05 ms per evaluation (3.01 -> 2.59 ms under load). Its
Swin layers still equal swin_model.py on sampled blocks and its image is
byte-identical to the previous accuracy set over 8 temporal frames.
On the gfx12 native-FP8 path the Swin layers kept GEMM activations as f16
in LDS and encoded them to e4m3 again at every operand load. They are now
encoded once where they are produced, with v_cvt_pk_fp8_f32, and stored as
bytes (SWIN_A8); the patch merge reads the stored output codes. The M
downsample kernels are built as wave64 on gfx12.
RX 9070 XT, 1280x720 -> 3840x2160, D3D12 harness, DLSS 310.7: whole
evaluation 5.9 ms -> 2.8 ms of GPU time with the GPU otherwise idle, 2.4 ms
at 80-98% GPU utilisation. Harness output is byte-identical to the previous
kernels; the accuracy build matches swin_model.py on sampled blocks.
Builds without D4R_FP8_WMMA (RDNA3, and RDNA4 with NativeFp8 off) compile to
the same instructions as before.
woutermans