Make the video speed/mem bench MoE-aware; add A14B + Hunyuan-720p families
Adds wan2.2-t2v-a14b and hunyuanvideo-1.5-720p to the bench family table and makes _apply_levers quantize / compile BOTH experts of a dual-expert MoE (Wan2.2-A14B) via a _SecondExpertView proxy that mirrors the loader's _SecondDiTView, so A14B latency and accuracy are measured on the real two-DiT path instead of only the first expert. Used to validate that every video family is on the fastest DiT quant scheme that is not less accurate than its alternative (B200, 512x320, 25 frames, 30 steps, no cache, LPIPS vs dense bf16): - Wan2.2-TI2V-5B fp8 49.9 ms/step vs int8 64.6 vs dense 59.8; LPIPS 0.129 vs 0.180 - Wan2.2-T2V-A14B fp8 195.8 ms/step vs int8 193.5 vs dense 369.1; LPIPS 0.288 vs 0.349 (both experts) - LTX-2 / LTX-2.3 fp8 133.5 ms/step vs int8 138.4 vs dense 204.7; LPIPS 0.026 vs 0.027 - HunyuanVideo-1.5 fp8 is black (denied, not localizable); int8 21.2 s vs dense+compile 15.4 s -- a memory saving at a speed cost, so int8 stays only because fp8 is impossible there. fp8 is faster than dense AND more accurate than int8 on all three Wan/LTX families (the two Wan ones via the condition_embedder exclude; LTX-2 needs none -- its conditioning has no zero-amax padding rows). The auto-ladder + deny + exclude already select exactly these schemes, so no scheme-selection change was needed; this commit is the bench faithfulness improvement that let the campaign confirm it.
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Daniel Han
parent
ff853c3977
commit
42a4c07c3c
1 changed files with 65 additions and 19 deletions
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@ -67,6 +67,17 @@ _FAMILIES: dict[str, dict[str, Any]] = {
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"vae_force_fp32": False,
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"guidance": 6.0,
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},
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"hunyuanvideo-1.5-720p": {
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"repo": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v",
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"vae_force_fp32": False,
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"guidance": 6.0,
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},
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# Wan2.2-A14B is a dual-expert MoE (transformer + transformer_2); _apply_levers quantizes both.
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"wan2.2-t2v-a14b": {
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"repo": "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
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"vae_force_fp32": True,
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"guidance": 5.0,
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},
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}
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@ -256,10 +267,32 @@ def _build_pipe(repo: str, force_fp32_vae: bool):
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return pipe
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class _SecondExpertView:
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"""Present ``pipe.transformer_2`` as ``.transformer`` so the single-DiT lever functions run on
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the second expert of a dual-expert MoE (Wan2.2-A14B) unforked -- mirrors the loader's
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_SecondDiTView (video.py). Attribute reads delegate to the real pipe except ``transformer``,
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and a ``transformer`` write (e.g. torch.compile reassigning it) is routed to ``transformer_2``."""
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def __init__(self, pipe):
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object.__setattr__(self, "_pipe", pipe)
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@property
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def transformer(self):
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return self._pipe.transformer_2
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def __getattr__(self, name):
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return getattr(self._pipe, name)
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def __setattr__(self, name, value):
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setattr(self._pipe, "transformer_2" if name == "transformer" else name, value)
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def _apply_levers(pipe, cfg: dict, *, fam_name: str, fam_obj, force_fp32_vae: bool, default_steps: int,
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logger=None) -> dict:
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"""Apply the configured levers with the loader's own argument values, in the loader's order:
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quant (dit -> te -> vae) THEN optimisation layers (cache -> attention -> speed)."""
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quant (dit -> te -> vae) THEN optimisation layers (cache -> attention -> speed). For a
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dual-expert MoE (pipe.transformer_2 present) every DiT-touching lever is applied to BOTH experts
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via _SecondExpertView, exactly like the loader, so A14B latency + accuracy are real."""
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from core.inference.diffusion_precision import quantize_text_encoders
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from core.inference.diffusion_vae_quant import quantize_vae
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from core.inference.diffusion_transformer_quant import quantize_transformer
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@ -274,22 +307,31 @@ def _apply_levers(pipe, cfg: dict, *, fam_name: str, fam_obj, force_fp32_vae: bo
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tgt = _target()
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engaged = {"dit": None, "te": None, "vae": None, "attn": None, "cache": None, "speed_optims": {}}
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# DiT quant (pipeline kind, resident): mutates pipe.transformer in place.
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# DiT-touching levers run per expert: [pipe] for a single-DiT family, plus a second-expert view
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# for a dual-expert MoE. Each view exposes the expert as ``.transformer``.
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views = [pipe]
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if getattr(pipe, "transformer_2", None) is not None:
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views.append(_SecondExpertView(pipe))
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# DiT quant (pipeline kind, resident): mutates each expert's transformer in place.
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if cfg["dit"] not in ("none", "off"):
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engaged["dit"] = quantize_transformer(
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pipe, tgt, mode=cfg["dit"], family=fam_name, logger=logger
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)
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schemes = [
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quantize_transformer(v, tgt, mode=cfg["dit"], family=fam_name, logger=logger)
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for v in views
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]
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engaged["dit"] = schemes[0]
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engaged["dit_experts"] = schemes
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_empty()
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dit_quant_active = engaged["dit"] is not None
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# TE quant.
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# TE quant (once; text encoders are shared, not per-expert).
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if cfg["te"] not in ("none", "off"):
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engaged["te"] = quantize_text_encoders(
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pipe, tgt, mode=cfg["te"], family=fam_name, offload_active=False, logger=logger
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)
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_empty()
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# VAE quant (Wan force_fp32 pins dense inside quantize_vae regardless).
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# VAE quant (once; Wan force_fp32 pins dense inside quantize_vae regardless).
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if cfg["vae"] not in ("none", "off"):
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engaged["vae"] = quantize_vae(
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pipe, tgt, mode=cfg["vae"], family=fam_name, offload_active=False,
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@ -307,27 +349,30 @@ def _apply_levers(pipe, cfg: dict, *, fam_name: str, fam_obj, force_fp32_vae: bo
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speed = "default"
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speed_active = True
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# Step cache FIRST (compile keys fullgraph off an active cache).
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# Step cache FIRST (compile keys fullgraph off an active cache); per expert.
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cache_active = False
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if cfg["cache"] == "auto":
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cache_request = TC_FBCACHE if default_steps >= FBCACHE_MIN_STEPS else None
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if cache_request is not None:
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engaged["cache"] = apply_step_cache(
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pipe, mode=cache_request, threshold=None,
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quant_active=dit_quant_active, logger=logger,
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)
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for v in views:
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engaged["cache"] = apply_step_cache(
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v, mode=cache_request, threshold=None,
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quant_active=dit_quant_active, logger=logger,
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)
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cache_active = engaged["cache"] not in (None, "off")
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# Attention.
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# Attention (per expert).
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backend = select_attention_backend(tgt, cfg["attn"], speed_active=speed_active)
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engaged["attn"] = apply_attention_backend(pipe, backend, logger=logger)
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for v in views:
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engaged["attn"] = apply_attention_backend(v, backend, logger=logger)
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# Speed profile.
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# Speed profile (per expert; compiles each denoiser).
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if speed != "off":
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engaged["speed_optims"] = apply_speed_optims(
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pipe, tgt, is_gguf=False, family=fam_obj, speed_mode=speed,
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cache_active=cache_active, offload_active=False, logger=logger,
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)
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for v in views:
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engaged["speed_optims"] = apply_speed_optims(
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v, tgt, is_gguf=False, family=fam_obj, speed_mode=speed,
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cache_active=cache_active, offload_active=False, logger=logger,
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)
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engaged["_effective_speed"] = speed
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return engaged
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@ -452,6 +497,7 @@ def _run_config(name: str, cfg: dict, *, family: str, steps: int, width: int, he
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"family": family,
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"levers": cfg,
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"dit_scheme": engaged["dit"] or "dense",
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"dit_experts": engaged.get("dit_experts"),
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"te_scheme": engaged["te"] or "dense",
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"vae_scheme": engaged["vae"] or "dense",
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"attn": engaged["attn"] or "native",
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