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>
Encode 5.0 s -> 3.4 s per 1024x1024 frame in release on 16 threads (fused
softmax, queries scaled before the matmul, flat 2-D linear layers); a click
on an encoded frame is about 120 ms. Reference tests unchanged.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Weights loader with shape/finite validation (S2.10), patch embed, bicubic
positional embedding, window/global attention, query pooling. Reference test
(skips without weights): max abs error 2.6e-5 over all 12 blocks.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
