67beaede45
Phase 10.8: torchao/bnb quant sweep on iter1-independent. bf16 already
optimal; torchao int8-wo gives -19% VRAM at no F1 cost; all 4-bit
variants collapse (ModernBERT-large too quant-sensitive).
Phase 10.9: ONNX export + ORT eval. Legacy exporter only working path
(dynamo adds 56 Memcpy nodes); ORT fp32 -22% latency vs torch via
kernel fusion but bf16+flash-attn-2 still wins; fp16 broken on rotary;
dynamic int8 silently CPU-fallback + 0.5 F1 collapse.
Driver scripts wired to bun run py:quant / py:onnx; full reports at
results/eval/{quant,onnx}/REPORT.md.
164 lines
9.3 KiB
Markdown
164 lines
9.3 KiB
Markdown
# Quantization Sweep — iter1-independent ModernBERT-large
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**Date:** 2026-04-07
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**Checkpoint:** `checkpoints/finetune/iter1-independent/final/`
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**Hardware:** RTX 3090 (sm_8.6, 24 GB)
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**Eval set:** 1,200-paragraph v2 holdout, proxy gold = GPT-5.4 + Opus-4.6
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**Driver:** `python/scripts/quantize_sweep.py` (run via `bun run py:quant`)
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## Setup
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For each variant the *encoder* (ModernBERT-large backbone, 28 layers, 112
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nn.Linear modules) is converted to the target precision/scheme, while the
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attention pooler and the dual heads (category linear + 3 independent
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threshold MLPs) are kept in bf16. Heads are <0.3% of params and sit on
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already-distilled 1024-d representations — quantizing them buys nothing and
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risks the threshold margins that drive most of the spec error budget.
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For every variant we measure end-to-end inference on the full 1,200-paragraph
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holdout at batch=64, max_seq=512, after 5 warmup batches:
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- **encoder_mb** — sum of `param.numel() * param.element_size()` over the
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encoder. **Caveat:** for torchao tensor subclasses (`AffineQuantizedTensor`)
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this reports the *outer* dtype (bf16) rather than the int8 storage, so the
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790 MB figure for the torchao rows is an over-estimate; real on-disk
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storage is roughly half. The bnb 4-bit row (275 MB) is correct because
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`Params4bit` reports `uint8` element_size.
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- **ms/sample** — wall-clock per paragraph at batch=64
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- **peak VRAM** — `torch.cuda.max_memory_allocated()` over the timed run
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(encoder fwd + activations)
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- **F1 / QWK / ECE** — full eval pipeline reused from `src/finetune/eval.py`
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## Results
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| variant | enc MB | ms/samp | thru/s | VRAM MB | cat F1 (GPT) | spec F1 (GPT) | spec QWK | cat F1 (Opus) | spec F1 (Opus) | notes |
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|--------------------|-------:|--------:|-------:|--------:|-------------:|--------------:|---------:|--------------:|---------------:|--------------------------------|
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| fp32 | 1579 | 16.29 | 61 | 3504 | 0.9337 | 0.8943 | 0.9321 | 0.9227 | 0.8825 | sdpa (no flash-attn) |
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| **bf16 (baseline)**| 790 | 5.52 | 181 | 1741 | 0.9337 | 0.8952 | 0.9324 | 0.9227 | 0.8834 | flash-attn-2 |
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| fp16 | 790 | 5.54 | 181 | 1741 | 0.9337 | 0.8952 | 0.9324 | 0.9227 | 0.8834 | flash-attn-2 |
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| **torchao int8-wo**| ~395* | 6.08 | 165 | 1416 | 0.9345 | 0.8941 | 0.9330 | 0.9235 | 0.8815 | weight-only int8 |
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| torchao int8-dyn | ~395* | 9.67 | 103 | 1774 | 0.9336 | 0.8918 | 0.9315 | 0.9243 | 0.8827 | dyn act + int8 weight |
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| torchao int4-wo | — | — | — | — | — | — | — | — | — | requires `mslk>=1.0.0` |
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| bnb LLM.int8 | ~395* | 7.76 | 129 | 2135 | 0.9361 | 0.8986 | 0.9308 | 0.9235 | 0.8827 | mixed-precision outliers |
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| bnb nf4 (DQ) | 275 | 5.86 | 171 | 1287 | 0.3537 | 0.2205 | 0.2423 | 0.3576 | 0.2075 | **collapsed** |
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| bnb nf4 (no DQ) | 275 | 5.86 | 171 | 1287 | 0.3537 | 0.2205 | 0.2423 | 0.3576 | 0.2075 | **collapsed** |
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| bnb fp4 (no DQ) | 275 | 5.87 | 170 | 1287 | 0.1629 | 0.2085 | 0.2326 | 0.1686 | 0.1978 | **collapsed harder** |
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\*torchao subclass tensors report bf16 element_size; true storage ~395 MB.
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Per-variant detail (per-class F1, MCC, AUC, confusion matrices, calibration
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bins) is in `results/eval/quant/{variant}/metrics.json`. Aggregate row-level
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data is in `results/eval/quant/summary.json`.
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## Findings
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### 1. bf16 is already the production sweet spot
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Flash-attention-2 + bf16 gives **3.0× the throughput of fp32** (181 vs 61
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samples/sec) at **half the VRAM** (1.7 vs 3.5 GB) with bit-identical
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accuracy. This is what we already train and serve at; the sweep simply
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confirms there's no headroom in fp16/fp32 for this hardware.
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### 2. fp16 ≡ bf16 on Ampere
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Identical latency, identical VRAM, identical F1. RTX 3090 has matched
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bf16/fp16 throughput on tensor cores and the model has no overflow issues
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in either format. Pick whichever the loader prefers.
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### 3. torchao int8 weight-only is the only quantization variant worth shipping
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- **VRAM −19%** (1741 → 1416 MB) — meaningful for batched serving
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- **F1 essentially unchanged** (cat +0.0008, spec −0.0011 vs bf16 — both
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inside per-seed noise)
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- **Latency +10%** (5.52 → 6.08 ms/sample) — the int8 weight is dequantized
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to bf16 on the fly because RTX 3090 (sm_8.6) lacks the int8 tensor-core
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matmul kernel paths torchao would otherwise use; on H100/A100/Ada this
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same config would also be faster
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The accuracy delta is statistically nothing — well within the ±0.002 std we
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observed across the 3-seed ensemble. **This is the variant we'd ship as the
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"low-VRAM" deployment option.**
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### 4. torchao int8 dynamic activation: don't bother on this hardware
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−43% throughput (5.52 → 9.67 ms/sample) and *more* peak VRAM than bf16
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(1774 vs 1741 MB) because the per-batch activation quantization adds work
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without unlocking int8 tensor cores. Pure regression on Ampere.
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### 5. bnb LLM.int8: slowest int8, no accuracy upside
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- **+41% latency** (5.52 → 7.76 ms/sample) due to mixed-precision outlier
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handling
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- **+23% VRAM** (1741 → 2135 MB) — outlier columns are kept in fp16 plus
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scratch buffers
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- **F1 +0.0024 cat, +0.0034 spec** — within noise; not a real win
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bnb LLM.int8 was designed for LLM-scale models where outlier features
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dominate quant error; for an encoder of this size on a single 3090 it
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just trades performance for nothing.
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### 6. All 4-bit variants collapse — ModernBERT-large is too quant-sensitive
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Both nf4 (with and without double-quantization) and fp4 produce essentially
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random predictions:
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| variant | cat F1 | spec F1 | spec ECE |
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|---------|-------:|--------:|---------:|
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| nf4 | 0.354 | 0.221 | 0.434 |
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| fp4 | 0.163 | 0.209 | 0.443 |
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Per-layer dequantization is faithful — we verified that the dequantized
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weight of one MLP Wi layer differs from the original by mean 0.005 / max
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0.11 (sub-1% error). But the relative output drift on a single Linear is
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already ~98% (mean), and that error compounds across 28 transformer blocks
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+ GLU FFN paths until the [CLS]/pooled representation no longer carries
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the discriminative signal. The category head essentially collapses to a
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near-uniform prior (cat ECE 0.10 vs the 0.054 baseline) and the threshold
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heads collapse onto L1 because all three thresholds emit similar logits.
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The fact that **DQ vs no-DQ are bit-identical** at this scale tells us the
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nf4 weight indices are stable under absmax requantization (only ~5% of the
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weight bytes change, all in the metadata block) — the catastrophe is
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inherent to 4-bit weight precision on this architecture, not to a
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quantization-config knob.
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This is a real noteworthy null for the paper: **naive post-training 4-bit
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weight quantization is not viable for ModernBERT-large on this task**.
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Recovering 4-bit would require either (a) QAT, (b) per-channel calibration
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with a held-out activation distribution (GPTQ / AWQ-style), or (c) keeping
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the GLU FFN in 8-bit and only 4-bit'ing attention projections. None of
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these are reachable inside the remaining capstone time budget.
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### 7. torchao int4-wo: dependency hole
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torchao 0.17 requires `mslk >= 1.0.0` for the new `Int4Tensor.from_hp` path.
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Not installed in the lockfile and not worth chasing given the bnb 4-bit
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collapse — even if the kernel ran cleanly we'd expect the same compounding
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error pattern.
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## Recommendations
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| Use case | Variant | Why |
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|-----------------------------------|--------------------|-------------------------------------------------------------|
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| **Production / paper headline** | bf16 | Best of every dimension on this hardware |
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| **Low-VRAM batch serving** | torchao int8-wo | −19% VRAM, accuracy intact, only 10% latency penalty |
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| **Multi-GPU sharded serving** | bf16 | int8-wo's dequant overhead grows with replica count |
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| **Embedded / 4-bit** | not viable | Needs QAT or AWQ-style calibration; future work |
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## Paper-worthy notes
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1. **Quantization story** — bf16 is already the sweet spot; torchao int8-wo
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buys 19% VRAM with no accuracy cost; 4-bit fails. This adds another row
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to the speed/cost table.
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2. **Architecture-specific quant fragility** — ModernBERT-large's GLU FFN
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amplifies per-layer weight error across 28 blocks. This is a noteworthy
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counterpoint to the 4-bit-by-default LLM serving narrative and worth
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one paragraph in the discussion section alongside the DAPT and
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CORAL null results.
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3. **Hardware caveat** — int8 latency results would invert on
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Hopper/Ada/A100; the 3090 just doesn't have the matmul path. State the
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sm_8.6 caveat in the table caption.
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## Reproduce
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```bash
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# from repo root
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bun run py:quant
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# writes to results/eval/quant/{summary.json, REPORT.md, <variant>/metrics.json}
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```
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Run time: ~5 minutes total (most spent in fp32 + torchao build steps).
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