- diffusion_attention: arch-gate FlashAttention 2 to Ampere (SM80)+ in both the
primary selector and the heterogeneous-replica guard (it crashed on pre-Ampere).
- diffusion_cfg_parallel: convert boolean attn masks to additive bias before the direct
cuDNN op so partial masks match F.scaled_dot_product_attention; make proxy disable_cache
transactional (clean both branches, mark broken, surface a reload-required error).
- diffusion_cache: fail closed when a magcache step-count resize or below-threshold
disable cannot remove the old cache; surface a failed enable+cleanup instead of a false
uncached None.
- video: roll back earlier experts when a later expert raises in the all-or-none step-cache
loop; fail the load when the primary-only cache cannot be re-engaged through the
CFG-parallel proxy; validate transformer_cache_quality and cfg_parallel before the worker.
- scripts: place the fp8 ablation pipeline on CUDA; fail closed on a failed magcache resize
in the speedmem bench; label OOM distinctly in the SDPA mask probe.
- tests: regressions for the FA2 arch gate, transactional proxy disable, all-or-none
exception rollback, magcache fail-closed transitions, and enable+cleanup failure.
Review round follow-ups:
- Drop the machine-specific HF_HOME defaults from the four bench /
reproduction scripts (fp8_layer_ablation, hunyuan_int8_profile,
quant_accuracy_sweep, video_speedmem_bench); they pointed at a private
workspace cache and broke the scripts on any other machine. The
standard HF_HOME env override still applies.
- Correct the vae_quant 'auto' descriptions (image + video request
fields, select_vae_quant_scheme docstring, loader comment) to match
the shipped ladder: auto engages layerwise fp8 only; fp8_dynamic is an
explicit opt-in and is never picked automatically.
- Enforce _TE_FAMILY_SCHEME_DENY on the explicit text-encoder path too,
gating the final concrete mode (so an int8 -> fp8 fallback is
re-checked), matching the table's documented contract and the VAE
module's behavior. Covered by a new test.
Also merges origin/image-generation (single-GPU fit-budget fix) to keep
the stacked head self-consistent.
The Wan fp8 black frame was root-caused (scripts/fp8_layer_ablation.py,
measured on B200 with the production torch._scaled_mm path): per-row fp8
scales each activation row by row_amax/448, and the text prompt is padded to
512 tokens (~all padding for a short prompt), so condition_embedder's text
embedder divides a zero padding row by a zero scale, which infs and renders
every frame black. That embedder's bias makes every downstream row non-zero,
so the whole 30-block attn1/attn2/ffn stack is fp8-clean (fp8-except-
condition_embedder measured cosine 0.9998 vs bf16, 0 non-finite; fp8-
everywhere is 100% non-finite).
So the blanket fp8 deny was heavier than needed for Wan. Remove fp8 from the
Wan deny and keep only condition_embedder in bf16 via a new
_FP8_FAMILY_EXCLUDE_NAME_TOKENS; auto now restores fp8 (the Blackwell ladder
head) for Wan2.2-TI2V-5B and -T2V-A14B (shared DiT class and padded-text
conditioning). Full-generation check (512x320, 25 frames, 30 steps, cache on
and off): mixed-fp8 is non-black (mean luma 182.6 vs dense 181.2), more
accurate than int8 (LPIPS 0.129 vs 0.180 no-cache, 0.224 vs 0.251 with
FBCache), faster (49.9 vs 64.6 ms/step; int8 was a per-step regression vs the
59.8 ms/step dense), at the same memory (19.34 GB, both -20% vs dense).
HunyuanVideo-1.5 keeps the fp8 deny: its MMDiT masks the padding text tokens
to zero inside every block, so the per-block context stream (add_*_proj /
to_add_out / ff_context) regenerates zero rows layer after layer (fp8 on only
the main blocks is 100% non-finite) so no small exclude set exists and int8
stays. mxfp8 / nvfp4 remain denied for Wan (same per-row scaled_mm family, not
separately validated).
exclude_tokens_for_scheme now takes an optional family, threaded through the
runtime quantiser and the offline prequant builder + validator so offline ==
runtime (a stale Wan fp8 checkpoint baked without the exclude is rejected and
re-quantised rather than loaded). Adds scripts/fp8_layer_ablation.py (the
per-layer ablation probe) and a mean-luma black-frame metric plus mixed-fp8
vs int8 configs to the video bench.
