Memory attention (1.8e-5 vs PyTorch) and the memory encoder (2.6e-6) as wgpu kernels,
convolution and layer-norm+GELU kernels, VideoModel::load_auto picks the GPU when
there is one (EFFECTCRAFT_SAM2_DEVICE overrides) and falls back to the CPU on any GPU
error; disable_gpu switches it off mid-clip. One process-wide device (several at once
crashed the Vulkan loader). A tracked frame: 4.3 s CPU, 0.7 s on the RX 7900 GRE.
Clips tracked on the GPU match Sam2VideoModel (worst logit error 0.0032).
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Candle adds a bias on one thread; the Linear layers now add it (and apply GELU
for the MLP in) in one parallel pass. Encode 2.4 s -> 2.1 s, memory attention
2.4 -> 2.3 s; reference tests unchanged. gpu_matmul example: a tiled WGSL
matmul on the RX 7900 GRE reaches 1.9-3.6 TFLOPS against 0.16-0.47 for the CPU.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Candle's gelu_erf runs on one thread and was a third of the Hiera block time.
A rayon CustomOp1 calls the same erf per element (bit-identical, tested). All
reference tests unchanged. op_bench example times the block's operations.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Mask down-sampler, feature fuser and projection, sine position encoding,
occlusion embedding and bfloat16 rounding of stored memories. Max abs error
3.5e-6 on the memory features, 6e-8 on the position encoding.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
