Add opt-in FP8 DiT TensorRT engine for SA3-medium

optimized/tensorRT/.gitignore

@@ -12,6 +12,11 @@ onnx/
 *.onnx
 *.onnx.data
 
+# FP8 calibration data — captured by build/make_calib.py from the model
+# checkpoint, a reproducible producer artifact (~hundreds of MB). Regenerated
+# on demand, never committed.
+*.calib.npz
+
 # T5 tokenizer — generally ignored (downloaded under models/<arch>/t5gemma/
 # alongside the engine for legacy fallback), but the canonical copy ships
 # bundled at scripts/tokenizer.json (arch-agnostic, 34 MB) so the build path
@@ -36,3 +41,7 @@ __pycache__/
 # But keep __pycache__/ ignored even under build/ (the un-ignore above would
 # accidentally re-include build/__pycache__/ otherwise).
 build/**/__pycache__/
+# Same for make_calib.py's captured calibration npz (the *.calib.npz rule
+# above is otherwise shadowed by the build/ un-ignore — make_calib writes it
+# next to the build scripts by default).
+build/**/*.calib.npz

optimized/tensorRT/build/README.md

@@ -69,6 +69,10 @@ python build_from_onnx.py same-s-decoder-fp32   # canonical ONNX is already FP32
 python build_from_onnx.py sa3-m-fp32            # reads HF dit.onnx (already FP32)
 python build_from_onnx.py all-fp32              # every FP32 target
 python build_from_onnx.py all-both              # canonical + FP32
+
+# FP8 variant — opt-in, DiT-only. ~1.8x faster steps than FP16-mixed.
+# Pair with `sa3_trt --precision fp8` at inference (fp8 DiT + fp16mixed decoder).
+python build_from_onnx.py sa3-m-fp8             # reads HF dit_fp8.onnx (ModelOpt PTQ)
 ```
 
 ### Consumer deps
@@ -148,6 +152,33 @@ This wraps every RMSNorm chain, attention `Softmax`, and the RoPE region in `Cas
 
 Naive `BuilderFlag.FP16` (without the surgery) catastrophically overflows in RMSNorm variance + attention softmax — the islands are mandatory. BF16 was tried earlier and compounds quantisation error over 8 sampling steps (cos-sim drifts from 0.99 single-step to 0.81 final-latent vs PT FP32) — audibly degraded.
 
+### FP8 DiT (opt-in, ~1.8x)
+
+`build_dit_fp8.py` extends the FP16-mixed recipe with a ModelOpt FP8 GEMM trunk: it takes the `dit_fp16mixed.onnx` plus a calibration `.npz` (real DiT inputs across the pingpong schedule) and produces `dit_fp8.onnx`: fp8 weight/activation Q/DQ on the MatMuls, the same FP32 islands re-applied (plus the conditioning front-end, which must stay FP32 or the t>=0.984 timestep features flush), and per-channel weight scales. Validated on sa3-m vs the FP16-mixed engine over the 47 reprompt Music prompts x 8 sigmas (L=646): worst single-step latent cosine 0.9982, 8-step compounded euler final-latent cosine mean 0.953 / median 0.957 / worst 0.873 over the 47 prompts (the compounded rollout is chaotic at the early sigmas, so judge by the distribution and by ear; decoded audio under the production pingpong sampler tracks the FP16-mixed generation at ~0.90 RMS-curve correlation), ~10.6-11.0 ms/step (vs ~18.7-19.4), ~1.8x. TRT tactic selection is nondeterministic per build; if a fresh engine benches noticeably slower, rebuild it. Under the stochastic pingpong sampler it yields a different but comparable sample.
+
+First capture the calibration data from the model checkpoint with `make_calib.py` (drives the model's own pingpong `generate()` to record real DiT inputs across the sampling schedule; prompts come from the repo's own `interface/reprompt.py` Music examples, the deployment-matched reprompt format):
+
+```bash
+python make_calib.py \
+  --model-config <MODELS_ROOT>/SA3-M-hf/model_config.json \
+  --checkpoint   <MODELS_ROOT>/SA3-M-hf/model.safetensors \
+  --out          sa3-m.calib.npz
+```
+
+Then build the engine:
+
+```bash
+python build_dit_fp8.py \
+  --input  $HF/onnx/sa3-m/dit_fp16mixed.onnx \
+  --calib  sa3-m.calib.npz \
+  --onnx   $HF/onnx/sa3-m/dit_fp8.onnx \
+  --engine ../models/$ARCH/sa3-m/dit_fp8.trt
+```
+
+`make_calib.py` needs only the repo + checkpoint (`torch`, `numpy`, `stable_audio_3`). `build_dit_fp8.py` additionally requires `nvidia-modelopt` + `onnxruntime-gpu` (the calibration-repair pass); consumers compile the published `dit_fp8.onnx` with plain `build_from_onnx.py sa3-m-fp8` (STRONGLY_TYPED, no ModelOpt, no calibration).
+
+> **Not yet on HF.** `dit_fp8.onnx` + `dit_fp8.onnx.data` are not in the model repo yet, so `build_from_onnx.py sa3-m-fp8` and `sa3_trt --precision fp8` 404 until a producer run uploads them (under exactly those filenames). The consumer recipe and `--precision fp8` plumbing land here so the wiring is reviewed; the artifact upload is the follow-up step.
+
 Each script also writes the ONNX to `<HF_REPO>/onnx/<engine>/<file>.onnx`. After all 8 are done:
 
 ```bash
@@ -166,6 +197,8 @@ git push
 | `build_from_onnx.py` | One target → download ONNX from HF + compile to TRT. **For the SA3 DiTs, pulls `dit_fp16mixed.onnx` (the pre-processed island-wrapped graph)** so the consumer just needs to invoke `STRONGLY_TYPED` compilation — no `onnx-graphsurgeon` required | consumer |
 | `build_dit_profile.py` | Build a DiT with custom `(min, opt, max)` profile shapes (experimental — short-form / fixed-shape variants). Operates on either ONNX flavor. | consumer |
 | `build_dit_fp16mixed.py` | **Producer-side** ONNX surgery: takes the canonical FP32 `dit.onnx`, finds RMSNorm chains + attention `Softmax` + RoPE region, wraps each in `Cast(FP32) ↔ Cast(FP16)` islands, converts non-island weights to FP16, and writes both the modified `dit_fp16mixed.onnx` AND the TRT engine. Only re-run when the model retrains or the island recipe changes. Requires `onnx` + `onnx-graphsurgeon`. | producer |
+| `make_calib.py` | **Producer-side** FP8 calibration capture: drives the model's own pingpong `generate()` and records the six DiT engine inputs across the schedule into a `*.calib.npz` for `build_dit_fp8.py`. Needs only the checkpoint (`torch` + `stable_audio_3`). | producer |
+| `build_dit_fp8.py` | **Producer-side** FP8 trunk on top of `dit_fp16mixed.onnx`: ModelOpt FP8 PTQ (MatMul/Gemm, max calibration from a `.npz`), restores ModelOpt-corrupted initializers + recalibrates activation scales, re-applies the FP32 islands (incl. the conditioning front-end), and per-channel weight scales. Writes `dit_fp8.onnx` + the TRT engine. ~1.8x faster steps than FP16-mixed. Requires `nvidia-modelopt` + `onnxruntime-gpu`. | producer |
 | `build_t5gemma.py` | Trace + export T5Gemma encoder ONNX + build TRT | producer |
 | `build_same_s_decoder.py` | Trace + export SAME-S decoder ONNX + build TRT | producer |
 | `build_same_s_encoder.py` | Trace + export SAME-S encoder ONNX + build TRT | producer |

optimized/tensorRT/build/build.py

@@ -93,6 +93,13 @@ def _from_onnx(name):
     {"label": "[opt-in] DiT sm-sfx FP32",
      "command": _from_onnx("sa3-sm-sfx-fp32"),
      "outputs": ["sa3-sm-sfx/dit_fp32.trt"]},
+    # FP8 variant, opt-in, DiT-only. ~1.8x faster steps than FP16-mixed
+    # (ModelOpt PTQ; producer build_dit_fp8.py). A different but comparable
+    # sample under the stochastic pingpong sampler.
+    {"label": "[opt-in] DiT medium FP8 (~1.8x)",
+     "command": _from_onnx("sa3-m-fp8"),
+     "outputs": ["sa3-m/dit_fp8.trt"],
+     "opt_in": True},  # built only by number, never via "Build all missing"
 ]
 
 
@@ -134,12 +141,19 @@ def render_menu(arch: str, arch_dir: Path) -> list[bool]:
             size_s = fmt_size(sz) if sz >= 0 else f"{DIM}(missing){RESET}"
             print(f"        {tick}  {DIM}{rel}{RESET}  {size_s}")
 
-    n_missing = built_flags.count(False)
+    # "Build all missing" covers default targets only; opt-in variants (FP8)
+    # are built by number, so they are counted and reported separately.
+    n_missing = sum(1 for t, ok in zip(TARGETS, built_flags)
+                    if not ok and not t.get("opt_in"))
+    n_optin_missing = sum(1 for t, ok in zip(TARGETS, built_flags)
+                          if not ok and t.get("opt_in"))
     print()
     if n_missing == 0:
-        print(f"  {BOLD}{GREEN}[A]{RESET} Build all missing  {DIM}(nothing missing — all engines built){RESET}")
+        print(f"  {BOLD}{GREEN}[A]{RESET} Build all missing  {DIM}(nothing missing — all default engines built){RESET}")
     else:
         print(f"  {BOLD}{YELLOW}[A]{RESET} Build all missing  {DIM}({n_missing} target(s)){RESET}")
+    if n_optin_missing:
+        print(f"      {DIM}(+{n_optin_missing} opt-in target(s) not in [A] — build by number){RESET}")
     print(f"  {BOLD}{DIM}[Q]{RESET} Quit")
     return built_flags
 
@@ -180,7 +194,9 @@ def main() -> int:
             return 0
 
         if choice in ("a", "all"):
-            missing = [t for t, ok in zip(TARGETS, built_flags) if not ok]
+            # Opt-in variants (FP8) are excluded; build them by number.
+            missing = [t for t, ok in zip(TARGETS, built_flags)
+                       if not ok and not t.get("opt_in")]
             if not missing:
                 print(f"  {DIM}Nothing to build.{RESET}")
                 continue