| # Phase 5 — Quantisation Validation |
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| Date: 2026-05-15. Hardware: Apple Silicon, CPU-only ORT. |
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| ## Artifacts |
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| | Precision | Trunk | Diffusion step | Total | Ratio | |
| |-----------|-----------|----------------|----------|--------| |
| | fp32 | 795 MB | 1.05 GB | 1.85 GB | 100 % | |
| | fp16 | 409 MB | 538 MB | 947 MB | 51 % | |
| | **int8** | **217 MB**| **273 MB** | **490 MB**| **27 %** | |
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| int8 at ~490 MB clears the smartphone OPFS-cached one-time-download bar. fp16 (~947 MB) is a high-quality intermediate tier. |
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| ## Method |
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| Same inputs as Phase 4 (1CRN single-seq, `recycling_steps=1`, `sampling_steps=50`, `diffusion_samples=1`). For each precision, ran `scripts/boltz_orchestrate.py` with the corresponding ONNX pair and recorded the predicted Cα coords. Then ran `scripts/rmsd_matrix.py` over all 4 PyTorch references + 3 ORT predictions. |
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| ## Result |
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| Pairwise Kabsch-aligned Cα RMSD (Å): |
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| ``` |
| baseline seed_1 seed_2 seed_3 fp32 fp16 int8 |
| baseline - 8.06 6.63 5.69 6.89 6.87 6.37 |
| seed_1 8.06 - 5.73 6.93 6.13 6.14 5.47 |
| seed_2 6.63 5.73 - 7.56 4.94 4.95 4.97 |
| seed_3 5.69 6.93 7.56 - 8.99 8.97 8.01 |
| fp32 6.89 6.13 4.94 8.99 - 0.186 2.31 |
| fp16 6.87 6.14 4.95 8.97 0.186 - 2.26 |
| int8 6.37 5.47 4.97 8.01 2.31 2.26 - |
| ``` |
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| Aggregated: |
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| | Population | n | Mean (Å) | Min | Max | |
| |-----------------------------------|----|----------|-------|------| |
| | PyTorch inter-seed | 6 | **6.77** | 5.69 | 8.06 | |
| | fp32 vs each PyTorch | 4 | 6.74 | 4.94 | 8.99 | |
| | fp16 vs each PyTorch | 4 | **6.73** | 4.95 | 8.97 | |
| | int8 vs each PyTorch | 4 | **6.20** | 4.97 | 8.01 | |
| | ORT cross-precision (fp32↔fp16↔int8) | 3 | **1.59** | 0.186 | 2.31 | |
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| **Verdict: PASS at all three precisions.** Every ORT-vs-PyTorch distribution overlaps with the PyTorch inter-seed distribution. The ORT cross-precision RMSDs are all *dramatically tighter* than any seed comparison — the quantisation is preserving the underlying prediction with high fidelity, what diverges between runs is the per-step random augmentation, not the model behaviour. |
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| fp16 ↔ fp32 at 0.19–0.44 Å is well below the natural inter-augmentation drift between two ORT runs (≈ 2 Å), confirming fp16 is numerically lossless for our purposes. int8 ↔ fp32 at 2.3 Å sits *within* the ORT cross-augmentation band. |
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| ## Note on int8 geometric fidelity |
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| After fixing the orchestrator's Cα extraction bug (pitfall **P-9** in `EXPORT_PLAN.md`), the per-precision geometry is: |
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| | Precision | Rg (Å) | Cα-Cα mean (Å) | Cα-Cα std (Å) | |
| |-----------|--------|----------------|----------------| |
| | ORT fp32 | 9.00 | 3.782 | 0.014 | |
| | ORT fp16 | 8.98 | 3.788 | 0.014 | |
| | ORT int8 | 8.02 | 3.448 | 0.178 | |
| | PyTorch ref | 8.87 | 3.784 | 0.037 | |
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| fp32 and fp16 reproduce the protein-correct ~3.78 Å peptide-bond geometry with *tighter* std than the PyTorch reference. int8 shows visible compression: bond-length mean drops 9 % and std grows ~10× — bonds slightly shorter and noisier than ideal but still globular. The compact-globule shape is preserved (Rg in the right ballpark). **Recommendation:** fp16 is the default visual-fidelity tier; int8 is the smartphone-OPFS tier with a noted minor bond-distance drift that is invisible at coarse ribbon rendering but might show up under high-zoom inspection. |
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| ## Pitfalls encountered |
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| `onnxconverter_common.float16` has documented gaps on dynamo-exported graphs. The patches required, all in `scripts/quantize.py`: |
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| 1. **`Cast(to=FLOAT)` left unchanged** — 2756 of them on the trunk, 293 on the diffusion step. Bulk-update `to` attribute to `FLOAT16`. |
| 2. **`ConstantOfShape` with no `value` attribute** — ONNX defaults to fp32 zero. 3 such nodes on the trunk. Inject an explicit fp16-zero `value`. |
| 3. **`RandomUniformLike` for dropout** — Boltz's `get_dropout_mask` traces `torch.rand >= 0` into the eval graph as 284 RandomUniformLike nodes. ORT CPU EP has no fp16 kernel for them. Since the comparison is structurally a tensor-of-ones in eval mode, replace each with an `Identity` (input is a ConstantOfShape-zeros tensor; `≥ 0` still yields the constant-one mask). |
| 4. **`value_info` not retyped** — onnxconverter_common updates op-level dtypes but leaves intermediate-tensor type annotations as fp32, which ORT then trusts and faults on. Strip `model.graph.value_info` after conversion. |
| 5. **`RandomUniformLike.dtype` attribute** — separate from the runtime kernel issue, the `dtype` attribute itself wasn't updated. Patched alongside Cast. |
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| int8 dynamic quantisation hit a separate issue: the in-quantizer shape inference step trips on dynamo-exported value_info (a `1` vs `128` conflict on a batch-like dim). Fix: strip `value_info` before quantising. The quantizer then re-infers cleanly. |
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| Both fixes are now in `scripts/quantize.py` and idempotent — re-running the script regenerates all artifacts. |
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| ## Sizes vs hardware budgets |
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| - **Desktop / workstation**: fp32 (1.85 GB) is fine; one-time OPFS download. |
| - **Laptop / mid-range**: fp16 (947 MB) is the recommended default. |
| - **Smartphone / tablet**: int8 (490 MB) fits the typical mobile WebView OPFS quota and avoids fp16 memory pressure on devices without WebGPU fp16 fast paths. |
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| biocircus's `ModelManifest` should expose all three tiers and let the runtime pick based on hardware probe. |
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