Replace per-sample CUDA kernels (16,384 launches per GEMM) with batched
cublasLtMatmul — ONE call per GEMM layer. Forward pass uses 3 GEMMs +
SiLU + bias_add + expectile_loss. Backward pass uses 5 GEMMs + SiLU
backward + bias_grad_reduce. Eliminates grads_per_sample buffer (was
27MB), tiled backward loop, and weight_grad_reduce kernel.
Constructor now accepts Arc<SharedCublasHandle> instead of bare stream,
sharing the cuBLAS handle with the trunk forward/backward pipeline.
Eight IqlGemmDesc descriptors are cached at init with heuristic algo
selection for CUDA Graph compatibility.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
d799bfafbe