inference-quantize-calibrate

# inference-quantize-calibrate > **Fast model loading (always-applied):** when staging a base checkpoint or standing up a vLLM deploy, never load 100s-of-GB single-stream via s3fs FUSE (a GLM-5.1-sized model takes ~50 min that way. See `docs/METHODOLOGY.md`). Prefer, in order: a fast model-loading endpoint when reachable -> parallel multipart to local NVMe (`server/tools/stage-model-parallel.py`) -> `runai_model_streamer` (`--load-format runai_streamer`) -> tensorizer. Flag a slow load loudly: if effective rate < ~500 MB/s on a large model, STOP and switch. Details: `server/docs/inference-fast-model-loading.md`. ## Purpose Produce a quantized checkpoint **from a base model** -- rather than relying on NVFP4 weights *pulled pre-quantized* from `nvidia/*-NVFP4` HF repos. This skill provides the calibrate + quantize recipe for a fresh model as a two-Job PTQ pipeline: 1. **Calibration-dataset prep Job** -- tokenizes a small calibration corpus (default: a few hundred samples of C4 / the model's chat template) into the format the quantizer's PTQ calibration loop expects. 2. **Quantize Job** -- runs the quantizer over the base weights with the calibration set, emitting quantized weights + per-layer activation/KV scales. Two pluggable backends: | Backend | Target | Use when | | --- | --- | --- | | **NVIDIA ModelOpt** (`modelopt`, default) | **NVFP4** | Matches the `nvidia/*-NVFP4` serving checkpoints (modelopt 0.45, `quant_algo=NVFP4`, group_size 16). | | **llm-compressor** | **FP8** (compressed-tensors) | FP8 weight quant for an FP8-tier deploy or an FP8-vs-NVFP4 A/B. | The output PVC is experiment-prefixed and the result feeds straight into a deploy + accuracy/perf gate. ## When to use - A new model has no published NVFP4/FP8 variant and you need quantized weights to serve it on Blackwell efficiently. - You want to reproduce / improve on a vendor NVFP4 checkpoint with your own calibration set (e.g. a domain-specific calib corpus). - The `inference-model-optimize` orchestrator reached Phase 4. Do **not** use this skill for: - Models that already have a good vendor NVFP4/FP8 checkpoint you trust -- just pull the vendor checkpoint (cheaper, byte-for-byte vendor recipe). - KV-cache dtype selection at serve time (`--kv-cache-dtype=fp8_e4m3`) -- that is a `vllm.extraArgs` lever, not weight PTQ. Tune it with [`inference-tune-sweep`](/plugins/profile-and-optimize/skills/inference-tune-sweep/SKILL.md). - Runtime activation-quant kernel work -- that is custom-kernel territory, not this PTQ pipeline. ## Example prompts - "Quantize MiniMax-M2.7 to NVFP4 with modelopt and validate accuracy." - "Run an FP8 llm-compressor PTQ on this model and A/B it against BF16." - "Produce NVFP4 weights for `zai-org/GLM-5.2` with a C4 calibration set." - `/inference-quantize-calibrate --base /models/base --backend modelopt --target nvfp4` ## Prerequisites The skill **fails closed** if any of these are not satisfied. 1. The **base** (BF16 or FP8) checkpoint reachable (HF id / S3) -- you quantize *from* the base, not the NVFP4 serving copy. On the current GB300 (4 GPU/node) a large MoE base may need TP across 2 NVLink-domain nodes. B200 fits 8/node. 2. Output persistence (cluster-profile aware): on plain-K8s (no storageclass) write to emptyDir + upload to S3-compatible object storage from inside the pod. On legacy Slurm-on-K8s, an experiment-prefixed PVC (never reuse a standing `*-cache` PVC). 3. Cluster access + a free GPU node: plain-K8s GB300 (default) via `kubectl` GPU requests. Legacy Slurm-on-K8s via `sinfo`/`squeue`. The quantize Job is GPU-resident for calibration forward passes. 4. The quantizer container image (modelopt or llm-compressor) -- the `quantize/` templates pin one. Air-gapped clusters mirror it first. 5. The explicit ack flag for the quantize Job (it consumes a GPU node). ## Workflow ### Phase 0: confirm intent + scaffold the quant bundle State back: `base` path, `backend` (`modelopt` | `llm-compressor`), `target` (`nvfp4` | `fp8`), calibration corpus + sample count, output PVC name (experiment-prefixed), and the experiment-id (== the parent run-id). Read the base `config.json` for arch + chat template so calibration uses the right formatting. The scaffolder lays down `quantize/`: - `quantize/00-calib-pvc.yaml` -- experiment-prefixed output PVC. - `quantize/01-calib-dataset-job.yaml` -- calibration-dataset prep Job. - `quantize/02-quantize-job.yaml` -- the PTQ quantize Job (backend-parameterized). - `quantize/quantize.py` -- the backend dispatcher (modelopt / llm-compressor). - `quantize/README.md` -- the per-bundle runbook. ### Phase 1: prepare the calibration set Apply `01-calib-dataset-job.yaml`. It tokenizes the calibration corpus with the model's tokenizer + chat template into the quantizer's expected JSONL/arrow and writes it to the output PVC. Gate: Job Complete + the calib file exists and has the requested sample count. ### Phase 2: run the quantize Job (ack-gated) ```text # gate the manifest first: verify-experiment-labels.sh <id-slug> quantize/02-quantize-job.yaml # then apply (the Job carries the ack/intent in an env flag the entrypoint checks): kubectl -n <slurm-namespace> apply -f quantize/02-quantize-job.yaml ``` The Job runs `quantize.py`: - **modelopt path**: `mtq.quantize(model, NVFP4_DEFAULT_CFG, calib_loop)` then export to a HF-layout NVFP4 checkpoint (`quant_algo=NVFP4`, group_size 16) + `hf_quant_config.json`, so vLLM auto-detects it exactly like a `nvidia/*-NVFP4` repo. - **llm-compressor path**: a `oneshot` FP8 recipe (compressed-tensors), emitting per-layer scales. Outputs land at `/models/quantized/target` on the experiment PVC. Gate: Job Complete + the output `config.json`'s quant method is the expected one (`modelopt`/`modelopt_fp4`/`NVFP4` for NVFP4, `compressed-tensors`/`fp8` for FP8) -- the same assertion the `03b` puller makes on a vendor checkpoint. ### Phase 3: validate the quantized weights (the gate that matters) Deploy the quantized weights (the parent orchestrator's Phase 5, or a standalone deploy off the experiment PVC) and run: - [`inference-model-eval`](/plugins/profile-and-optimize/skills/inference-model-eval/SKILL.md) (GPQA + MMLU-Pro) vs the **base** model's scores. - A perf A/B (throughput + median TPOT) vs the base via [`inference-tune-sweep`](/plugins/profile-and-optimize/skills/inference-tune-sweep/SKILL.md) / [`inference-perf-bench`](/plugins/profile-and-optimize/skills/inference-perf-bench/SKILL.md). Gate: accuracy delta within tolerance (default: no metric drops > 1.0 absolute point) **AND** a perf win. A quant that wins perf but fails accuracy is a **no-ship** -- report the regression and keep the base/vendor checkpoint. Record the accuracy + perf result as a baseline so future re-quants diff against it. ### Phase 3b: 3-way comparison vs the vendor NVFP4 (our-PTQ vs NVIDIA-PTQ) When a vendor `nvidia/*-NVFP4` (or `*-FP8`) checkpoint exists for the same model, "is our PTQ good enough" is the wrong question -- the decision is **which of the two NVFP4 checkpoints to ship**. Run a head-to-head **3-way comparison** across three variants on one pinned node: | Variant | Source | Role | | --- | --- | --- | | `base` | BF16/FP8 base | accuracy ceiling reference | | `ours` | our ModelOpt NVFP4 (Phase 2 output) | candidate | | `nvidia` | `nvidia/*-NVFP4` (pulled from HF) | incumbent baseline | Compare on three axes, each artifact-cited: 1. **Accuracy** -- [`inference-model-eval`](/plugins/profile-and-optimize/skills/inference-model-eval/SKILL.md) (GPQA + MMLU-Pro) for all three. Report `ours` and `nvidia` as deltas vs `base`. 2. **Perf** -- median TPOT + throughput, same-node + >=3 trials ([`inference-tune-sweep`](/plugins/profile-and-optimize/skills/inference-tune-sweep/SKILL.md) controlled A/B). 3. **Per-kernel** -- [`inference-kernel-ncu-profile`](/plugins/profile-and-optimize/skills/inference-kernel-ncu-profile/SKILL.md) on the dominant decode kernel of each, to explain *why* one wins (tensor-core engagement, achieved roofline %) rather than asserting it. Implement as a `perftunereport` campaign with **one cell per variant** (`base` / `ours` / `nvidia`) so the three land side-by-side in the atlas + PDF + perf-lake automatically, and the verdict ("ship ours" / "ship vendor" / "tie -> keep vendor") is a published, data-backed row -- not a claim. A common honest outcome is parity, in which case the vendor checkpoint wins on provenance. Record that. ### Phase 3c: NVFP4-MoE serving gotchas A quantized MoE checkpoint can fail to *serve* (or serve garbage) for reasons unrelated to PTQ quality. Three gotchas, each with the fix: 1. **Activation-capability gap.** The native FP4 MoE kernels (CUTLASS, FlashInfer trtllm-gen) only advertise a fixed activation set. A model whose expert FFN uses an unlisted activation (gemma4 = `gelu_pytorch_tanh` -> `MoEActivation.GELU_TANH`) is rejected at engine init even though the weights are fine. Fix: add the activation to the kernel class `_supports_activation` (CUTLASS routes non-SiLU through the generic `apply_moe_activation`, so it is often a Python-only add) + the FlashInfer `ACTIVATION_TO_FI_ACTIVATION` map (note: FlashInfer has no tanh-gelu variant -> trtllm maps GELU_TANH -> Geglu, an *approximation*. The exact path is CUTLASS). 2. **Intermediate alignment is TP-dependent.** The FP4 MoE kernels need the per-rank expert intermediate `%64` (and the fused gate+up `2N %128` for trtllm). `moe_intermediate_size` that is fine at TP=1 can break at TP=4 (gemma4: 704 -> per-rank 176, not %64) with a `NotImplementedError` (CUTLASS gated-pad) or `assert M%128==0` (FlashInfer). Two fixes: (a) **offline** zero-pad `moe_intermediate_size` up to a multiple of `64*TP` on the checkpoint (pure zero-insertion on the stored pre-swizzle tensors - numerically exact, no modelopt. The production answer, no runtime cost), or (b) a **runtime** gated per-shard pad in `prepare_nvfp4_moe_layer_for_fi_or_cutlass`. 3. **The numerical gate MUST use the chat template for `-it` models.** A bare `/v1/completions` prompt on an instruction-tuned model produces degenerate repetition by design ("is is is...") - this is NOT a quant bug. Gate with `/v1/chat/completions`. (On gemma4 the raw gate falsely reported 0/4 garbage while the chat gate returned correct answers 4/4.) Always include a known-good control (TP=1, or the BF16 base) when a gate fails, to attribute model-vs-gate. ### Phase 3d: fused-3D experts + the model-family registry The single biggest PTQ hazard for MoE is **fused 3D expert tensors**: some HF models store experts as one `nn.Parameter [num_experts, dim, dim]` instead of an `nn.ModuleList` of `nn.Linear`. A naive `mtq.quantize(model, NVFP4_DEFAULT_CFG, calib_loop)` walks `nn.Linear` modules and **SILENTLY SKIPS** the fused experts (~90%+ of the model) -> a fabricated partial checkpoint (NVIDIA/Model-Optimizer #1173). The generated `quantize/quantize.py` now carries a **model-family registry** + a post-quant **coverage guard** that handles this. Use it instead of hand-rolling: - **`MODEL_FAMILY` registry** (keyed by `config.json` `model_type`) auto-resolves the **recipe** + **exclude scope** per family. Match the `nvidia/*-NVFP4` sibling's `hf_quant_config.json` `exclude_modules` exactly (pull it from HF) so the A/B is apples-to-apples. Worked entries: `gemma4_text` (native-plugin, 6-pattern experts-only excludes, `needs_mm_assets`), `qwen3_moe` (generic, exclude `lm_head` + router `mlp.gate`), `nemotron_h` (generic, exclude the Mamba/attn `mixer.*` projections). Add a new family with one dict entry or pass `--exclude-pattern`. - **Recipes**: `generic` (ModuleList experts, OR fused experts a modern modelopt auto-registers a QuantModule for) | `native-plugin` (this script registers Gemma4TextExperts #1219 for the fused Gemma-4 MoE) | `fail-closed` (refuse a fused MoE). `generic` no longer *refuses* a fused model upfront -- it attempts and the **coverage guard** (experts quantized > 0) catches a true silent-skip, so it works for archs modelopt natively supports without a hand-written plugin. - **transformers version determines fused-vs-ModuleList.** The SAME model can load as ModuleList on transformers 4.x and as a fused-3D module on 5.x (Qwen3-30B-A3B is `Qwen3MoeExperts` fused-3D on transformers 5.5.4). If `generic` hits a coverage-0 FATAL on a fused load, either pin a transformers that exposes ModuleList experts, or use a modelopt build that registers that arch's QuantModule. ALWAYS read the MoE STRUCTURE DUMP the script prints first. - **Hybrid Mamba-MoE (Nemotron-H) needs `mamba-ssm` + `causal-conv1d` in the quant image** -- its transformers modeling code hard-imports `mamba_ssm` at load (vLLM serves it with its own kernels, but the transformers calibration load does not). The stock vLLM serving image lacks them AND no prebuilt Blackwell (sm_100) wheels exist -> **build them from source CPU-side** (CUDA 12.9 + nvcc cross-compile, `TORCH_CUDA_ARCH_LIST="9.0;10.0"`, `--no-build-isolation`, `*_FORCE_BUILD=TRUE`, ~10 min for both), cache the wheels in object storage, and have the quant Job `pip install` them. Worked: causal_conv1d 1.6.2 + mamba_ssm 2.3.2 on CUDA 12.9 / torch 2.11 / cp312. PROVEN: Nemotron-H quantized via the generic path (5934 experts) and reached vendor accuracy parity (gsm8k chat 0.69). - **Export `AttributeError: 'list' object has no attribute 'keys'`** -- some remote modeling code (Nemotron-H) declares `_tied_weights_keys` as a LIST, but transformers 5.x's `_get_tied_weight_keys` calls `.keys()` (expects a dict). The generated `quantize.py` normalizes list -> `{k: k}` for every module before `export_hf_checkpoint` (generic. Fixes any list-tied-weights model on newer transformers). ### Phase 3e: serve marlin-first. Calibrate with the natural-routing loop - **MARLIN first for ROBUSTNESS, but ALWAYS run the serve-backend MATRIX per-model -- do NOT conclude "marlin-only".** W4A16 marlin (dequants FP4->FP16) is the robust default that serves any NVFP4 export. But it is NOT the perf winner: on a SAME-NODE 3-way sweep, NVFP4-**cutlass** (W4A4 native FP4) ties FP8 and beats NVFP4-marlin by ~+21% at high throughput (Qwen3 c256), while marlin wins only the c16 memory-bound knee and FP8 wins c1 latency -- there is NO universal winner (see `docs/METHODOLOGY.md`). Cutlass degeneracy is **MODEL-SPECIFIC** (Gemma's `gelu_pytorch_tanh` + 704-pad), NOT a property of "our export" -- Qwen3 serves cutlass fine. So: test EVERY backend per model with a raw "2+2" coherence gate, then run the backend x concurrency matrix sweep (same-node, 3-trial). Never carry a backend verdict from one arch to another. - **A serve "FATAL"/"Engine core initialization failed" is a per-cell DEBUG target, not "blocked".** The real error is in the `(EngineCore pid=...)` worker subprocess (upstream of the APIServer "see root cause above" wrapper) -- read that traceback directly. One-line root causes that have hidden behind it: `ModuleNotFoundError: pytest` (marlin_fp4 -> cupy.testing lazy import, `pip install