engine/patches
bf16-out-prod.patch teaches ggml-cuda's out_prod to accept a BF16 src0, which is what lets the DiT trainer's --mirror bf16 keep frozen trainable-layer weights in BF16 instead of promoting the whole mirror to F32. engine/ggml/ is a git submodule, so a submodule update silently reverts it.
mm-backward.patch adds an env-gated alternative formulation for the MUL_MAT activation gradient in ggml.c's ggml_compute_backward. Upstream emits out_prod(src0, transpose(grad)), and ggml-cuda's OUT_PROD is F32-only — which forces the frozen weight to F32 and drags the forward mul_mat onto TF32 tensor cores too. With GGML_BACKWARD_MM=1 set, the backward becomes mul_mat(cont(transpose(src0)), grad) instead: mathematically and shape-wise identical, but dtype-agnostic, so a BF16 weight rides real BF16 tensor cores in both directions with no dequant. Measured on an RTX 5090 at ~1.7–1.8× per layer per step. With the env var unset the emitted graph is byte-identical to upstream.
ace-train's --bwd <outprod|mm> sets that env var; the Training Studio defaults both trainers to mm.
Reapply from the repo root — apply all of them, they touch disjoint files (ggml-cuda/*.cu vs ggml.c) so order does not matter:
for p in engine/patches/*.patch; do git apply --verbose "$p"; done
Verify they are still in place: powershell -File engine\verify-hooks.ps1 (Hook 7 greps out-prod.cu, Hook 8 greps ggml.c, both for their HOT-Step marker comments). CI reapplies them in the "Apply engine patches" step of every build job, using the same glob loop.