Add vec4 gemm!#7
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Adds vortx::linalg::{Activation (tanh + tanh_backward), Adam} and their shaders,
the GPU building blocks for MLP training (used by nexus RL demos / zealot-rl).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two vec4 changes to the tiled GEMM, both verified bit-exact (0.0) vs the scalar gemm_tiled: - gemm_tiled inner loop now does 4-wide vec4 FMAs (acc: [Vec4;4], bvec.mul_add(Vec4::splat(a), acc)) instead of 16 scalar MACs. - new gemm_tiled_vec4 shader + Gemm::dispatch_tiled_vec4 host: loads the A/B tiles from global memory with 128-bit vec4 transactions (host reinterprets the buffers as glamx::Vec4). Assumes contiguous row-major, single batch, and tile-filling dims (M%64, N%64, K%16); the host falls back to scalar gemm_tiled for transposed/odd-dim cases. Pulls in glamx. Measured on an RTX 5090: the compute-FMA vec4 is ~+12% (rust-gpu -> SPIR-V -> NVIDIA does not auto-vectorize the inner loop the way Metal does); the vec4 *global* loads are ~+0% because a tiled GEMM already amortizes global memory (load once into smem, reuse), so it is not bandwidth-bound. Lesson: for a tiled GEMM, vec4 the compute, not the global loads. Note: the one-line glamx Cargo.toml dependency is shared with the ELU and PPO-kernels branches; whichever lands first, the others need a trivial rebase of that line. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Two vec4 changes to the tiled GEMM, both verified bit-exact (0.0) vs the
scalar gemm_tiled:
bvec.mul_add(Vec4::splat(a), acc)) instead of 16 scalar MACs.
A/B tiles from global memory with 128-bit vec4 transactions (host
reinterprets the buffers as glamx::Vec4). Assumes contiguous row-major,
single batch, and tile-filling dims (M%64, N%64, K%16); the host falls
back to scalar gemm_tiled for transposed/odd-dim cases.
Pulls in glamx. Measured on an RTX 5090: the compute-FMA vec4 is ~+12%
(rust-gpu -> SPIR-V -> NVIDIA does not auto-vectorize the inner loop the
way Metal does); the vec4 global loads are ~+0% because a tiled GEMM
already amortizes global memory (load once into smem, reuse), so it is
not bandwidth-bound. Lesson: for a tiled GEMM, vec4 the compute, not the
global loads.