[2/3] Speed up layerwise GPTQ on MoE models by cjluo-nv · Pull Request #2864 · NVIDIA/Model-Optimizer

cjluo-nv · 2026-10-17T17:07:05Z

What does this PR do?

Type of change: new feature (performance)

Stack (merge in order): [1/3] #2840 GPTQ on HF fused MoE experts (merged) ← [2/3] #2864 speed-up ← [3/3] #2945 Hessian placement by measured headroom. The fused NVFP4 GPTQ kernel device fix, #2877, is standalone on main and can merge in any order.

Layerwise GPTQ on a large HF fused-MoE model took about 25 minutes per decoder layer: 20 hours for Qwen3.8-Flash-Next's gate_up experts (512 experts, 48 layers) and over a day for GLM-5.3-Flash. Profiling one Qwen-shaped layer showed the GPTQ weight update was only 9 s of that. The time went to forward passes, for three reasons this PR removes:

A forward nobody used. gptq() ran max_calibrate with the calibration forward before its own Hessian forward. Weight amax comes from the weights (weight_only_quantize), so the forward only feeds activation quantizers. GPTQ now passes skip_forward_without_activation_calib=True, as max and imatrix already do, and weight-only recipes run one forward instead of two.
Hessians on the CPU while GPUs sat idle. GPTQHelper moves a Hessian to the CPU when its weight's GPU is over 65% used. With the model sharded across 4 GB300s, the GPU holding the current layer was 79% used and two others were empty, so every expert Hessian went to the CPU. Each hook call then copied the whole matrix (26 MB for Qwen) to the host and rescaled it there. A single process now puts such a Hessian on the emptiest GPU it already holds memory on that is under the limit and has the Hessian's bytes free, and uses the CPU only otherwise. Only GPUs the process already uses are candidates, so no other job's GPU is touched; distributed runs keep the old rule, since another GPU may belong to another rank. update_hessian also folds its incremental average into one addmm_ instead of a separate scale pass and add pass over the matrix.
Batch 1. hf_ptq.py picked --batch_size 1 for layerwise runs because its auto batch-size probe runs a full-model forward. A layerwise batch only holds one layer's activations, so the default is now a token budget: 8,192 tokens, 16 samples at --calib_seq 512. HF fused experts run a Python loop over experts on every forward, so per-sample cost is almost flat in batch size: 250 ms per sample at batch 1.
A logged error per expert. Each expert's GPTQ helper logged its Hessian-weighted error, which costs two weight-by-Hessian matmuls and a host sync: 576 times per GLM-5.3-Flash layer. Expert helpers now skip it, and gptq() logs one line per experts module with how many experts it updated and how many calibration never reached. Dense layers keep their line.

Usage

No API change. hf_ptq.py users who want the old batching pass --batch_size 1:

python hf_ptq.py --pyt_ckpt_path <model> --recipe <layerwise GPTQ recipe> --batch_size 1

Testing

Phase timing on one Qwen3.8-Flash-Next-shaped layer, measured locally on an RTX PRO 6000. The layer has 512 experts, hidden 2560, intermediate 640 and top-10 routing; gate_up is IQ2_XXS GPTQ with 32 samples of 512 tokens:

	GPTQ total	forwards	expert updates
before, batch 1	29.6 s	2 x 9.8 s	9.2 s
after, batch 1	19.0 s	1 x 8.9 s	9.3 s
after, batch 16	11.2 s	1 x 1.0 s	9.3 s

A GLM-5.3-Flash-shaped layer at batch 16 (288 experts, hidden 4096, intermediate 2048, gate_up and down quantized): 21.4 s, of which 19.8 s is the expert updates.

End to end on oci-jhb (GB300 x4, Qwen3.8-Flash-Next, gate_up IQ2_XXS, layerwise GPTQ, 1,024 x 512 calibration tokens):

Qwen3.8-Flash-Next unless noted	GPTQ time per layer	layerwise calibration (48 layers; GLM 45)	whole job (load, calibrate, KL scoring)
before (batch 1)	1,206-1,663 s on linear-attention layers, up to 1,862 s on full-attention ones	~20 h	~21 h
this PR (batch 16)	29-35 s (first layer 65 s, incl. warm-up)	44 min	70 min
GLM-5.3-Flash before (batch 1, gate_up + down, 288 experts)	739-2,131 s	~25 h (projected)
GLM-5.3-Flash, this PR (batch 16)	37-44 s; 126-136 s on the last 8 MoE layers	55 min	79 min

With only the first and third changes, a layer whose Hessians went to the CPU took 474 s against about 80 s for one whose Hessians stayed on a GPU, which is what the second change fixes.

Qwen3.8-Flash-Next's 12 full-attention layers also need a fix outside ModelOpt. transformers' reference sparse-attention indexer (Qwen4ExpTextQSAIndexer) loops in Python over every (sample, query) pair, which adds about 340 s per pass over 1,024 x 512 tokens at any batch size. At 512-token calibration, every visible token fits its 2,048-token budget, so its output equals the attention mask it receives. The run above patched the indexer to return that mask directly. The patch is bit-identical to the loop, checked on padded and float masks, and is not part of this PR. Without it, those layers took 374 s each instead of 33 s.

KL divergence against BF16 for the same experiment (wikitext-2, 100 x 512 tokens, second half scored, 25,500 tokens; differences paired per chunk):

gate_up IQ2_XXS	mean KL	vs max
max	0.2971
imatrix	0.2863	-0.0108 +- 0.0023
GPTQ, this PR (batch 16)	0.2492	-0.0479 +- 0.0033 (GPTQ - imatrix: -0.0371 +- 0.0034)

On GLM-5.3-Flash (all experts), GPTQ in this PR reaches KL 0.6298 against 0.7211 for imatrix and 0.7402 for max; both models' results are on #2840.

On GLM, the last 8 MoE layers took about 3x longer. By then every GPU this process uses was over the 65% limit, so their Hessians went to the CPU. A limit based on absolute headroom would keep them on a GPU: 65% of a 284 GB GB300 leaves about 100 GB unused. That is a possible follow-up.

There is no batch-1 KL to compare against: the batch-1 GPTQ runs kept being preempted and were cancelled. Batch size changes results only through floating-point rounding: hf_ptq.py pads every calibration row to one length before batching, so batch 1 and batch 16 see the same pad tokens. On phi-2 (NVFP4 W4A4, layerwise, 128 x 512 cnn_dailymail calibration; KL to BF16 on wikitext-2, 40 x 512 tokens, paired per chunk), batch 16 minus batch 1 is -0.0058 +- 0.0032 for GPTQ and +0.0058 +- 0.0052 for local-Hessian, neither significant.

Unit tests:

The related unit tests (tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_ggml_imatrix,test_layerwise_calibrate,test_calib}.py, plugins/test_fused_experts.py, tests/unit/recipe) and tests/examples/hf_ptq/test_hf_ptq_args.py: all pass on CPU with transformers 5.18.
New test_gptq_runs_a_max_calibration_forward_only_for_activation_amax counts the calibration forwards: 1 for weight-only, 2 with an input quantizer. It fails without the change.
New test_layerwise_calibration_batch_reaches_the_dataloader drives quantize_main with the layerwise general/ptq/iq2_xxs recipe up to the calibration dataloader, without a checkpoint, and checks the batch it receives: 16 at 8,192 tokens, capped at the sample count, 1 for sequences over 8,192 tokens, and an explicit --batch_size 1 kept as is. It fails with the old batch-1 default. --calib_seq must now be positive (test_calib_seq_must_be_positive).
New GPU tests, which need 2 GPUs. CI's GPU runner has one and skips them, so I ran them on 2x RTX PRO 6000:
- test_hessian_of_a_full_gpu_goes_to_an_idle_gpu: a Hessian whose GPU reports over 65% used is allocated on another GPU and accumulates the same values. It fails without the change.
- test_hessian_of_a_full_gpu_falls_back_to_the_cpu: the Hessian goes to the CPU when the idle GPU is full, when it lacks the Hessian's bytes, and on any rank of a multi-rank run. The too-small case fails without the capacity check.
- All 14 tests in tests/gpu/torch/quantization/test_gptq.py pass, and all 15 with Launch the fused NVFP4 GPTQ kernel on its tensors' GPU #2877 on top.
[1/3] Run GPTQ on HF fused MoE experts #2840's bit-exact fused-vs-dense GPTQ test still passes. The fused addmm_ changes the Hessian's float rounding for every GPTQ user, but both paths share it.
Skipping the per-expert error log, on GLM-5.3-Flash (gate_up and down IQ2_XXS, layerwise, 1,024 x 512 tokens, 4x GB300): GPTQ time per MoE layer 41.4 s against 43.5 s before. KL to BF16 does not change: a Qwen3.8-Flash-Next run with this commit gives 0.249200, as before.

Not run: multi-rank GPTQ end to end (it keeps the old CPU rule), and hf_ptq.py layerwise on a dense model at the new default batch.

Before your PR is "Ready for review"

Is this change backward compatible?: ✅ for the API. hf_ptq.py's layerwise default batch changes from 1 to 16 at --calib_seq 512. Batches pad each sequence to the longest in the batch, and pad tokens feed Hessians and activation amax, so results move slightly, as they already do in non-layerwise runs. --batch_size 1 restores the old behavior.
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
Did you write any new necessary tests?: ✅
Did you update Changelog?: ✅
Did you get Claude approval on this PR?: ❌

Additional Information

The expert updates are now the largest remaining phase on GLM, about 20 s per layer. Encoding one 256-column group for all experts in a single call is bit-exact with per-expert calls and 18x faster per group. Batched Cholesky and batched GEMMs are not bit-exact, so a follow-up would batch only the encoder.

🤖 Generated with Claude Code

Summary by CodeRabbit

New Features
- Layerwise calibration now defaults to batches totaling up to 8,192 tokens, limited by available samples. Use --batch_size 1 for the previous batch size.
- GPTQ skips an unnecessary calibration pass when activation quantization does not require one.
- With CPU offloading enabled, GPTQ can use a less-utilized GPU for Hessian calculations when sufficient memory is available; otherwise, it uses the CPU.
- GPTQ now reports how many expert weights were updated and how many received no calibration tokens.
Bug Fixes
- Calibration sequence lengths of zero or less are now rejected.
Documentation
- Updated calibration guidance to explain the default batch size, performance tradeoffs, and batch size used in the documented run.

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📝 Walkthrough

The changes update HF PTQ’s default layerwise calibration batch size, allow GPTQ to skip an unnecessary max-calibration forward, and change how GPTQ places and updates Hessians. They also adjust fused-expert error logging and reporting.

Changes: GPTQ Calibration

Layer / File(s)	Summary
Layerwise calibration batch sizing `examples/hf_ptq/hf_ptq.py`, `tests/examples/hf_ptq/test_hf_ptq_args.py`, `docs/source/announcements/20260928-single-gpu-ptq.rst`, `CHANGELOG.rst`	The example derives the default layerwise batch size from an 8,192-token budget, capped by the available calibration samples and bounded below by one. Argument parsing rejects non-positive `--calib_seq` values. Tests check default sizing, explicit batch sizes, and invalid sequence lengths. The announcement and changelog describe the updated behavior.
Conditional GPTQ max calibration `modelopt/torch/quantization/model_calib.py`, `tests/unit/torch/quantization/test_gptq.py`	GPTQ opts to skip the max-calibration forward when no enabled activation quantizer needs data-driven amax. Unit tests check that the forward loop runs once for weight-only quantization and twice when input activation quantization is enabled. Fused-expert handles suppress per-expert error logging, and GPTQ reports updated and uncalibrated expert-weight counts.
Hessian placement and accumulation `modelopt/torch/quantization/utils/calib_utils.py`, `tests/gpu/torch/quantization/test_gptq.py`	When CPU offloading is enabled, GPTQ may place Hessians on a less-used GPU if it meets the memory conditions. Hessian updates use device-aware matrix operations. GPU tests check placement, fallback conditions, and accumulated values.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant GPTQ
  participant max_calibrate
  participant ActivationQuantizers
  GPTQ->>max_calibrate: Request max calibration with conditional forward enabled
  max_calibrate->>ActivationQuantizers: Check for enabled quantizers needing data-driven amax
  ActivationQuantizers-->>max_calibrate: Report whether activation calibration needs data
  max_calibrate-->>GPTQ: Skip or run max-calibration forward

Suggested reviewers: edwardf0t1, sychen52

Merge Risk: 🟡 Moderate · up to da924

Models that fit the previous default batch may run out of memory during layerwise calibration. Users can pass --batch_size 1, but the default should be made safe before merging unless that risk is explicitly accepted.

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Failed checks: 1 warning

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Title check		The title clearly identifies the main change: performance improvements for layerwise GPTQ on MoE models. The [2/3] prefix adds minor context but does not reduce clarity.

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codecov · 2026-10-19T18:28:24Z

Codecov Report

❌ Patch coverage is 72.72727% with 9 lines in your changes missing coverage. Please review.
✅ Project coverage is 79.08%. Comparing base (414268f) to head (5487343).

Files with missing lines	Patch %	Lines
modelopt/torch/quantization/utils/calib_utils.py	64.00%	9 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2864      +/-   ##
==========================================
- Coverage   79.49%   79.08%   -0.41%     
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  Files         660      660              
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github-actions · 2026-10-21T06:34:42Z

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**Stack (merge in order):** [1/3] #2840 GPTQ on HF fused MoE experts ← [2/3] #2864 speed-up ← [3/3] #2877 fused NVFP4 GPTQ kernel device fix. ### What does this PR do? Type of change: new feature GPTQ only collected Hessians for modules with a 2-D `weight` and one `weight_quantizer` (`is_quantized_linear`), so HF fused MoE experts (one 3-D `gate_up_proj`/`down_proj` parameter with a `ModuleList` of per-expert weight quantizers) were skipped. They fell back to round-to-nearest, or to imatrix in the IQ recipes, which gets only part of GPTQ's gain: on a dense model, imatrix alone closes 53–63% of the gap from the plain encode to GPTQ. In an MoE those experts are most of the weights. - `gptq()` now builds one `GPTQHelper` per expert and projection. Its Hessian comes from a forward hook on the projection's shared input quantizer, keyed by `_current_expert_idx`, so it holds exactly the tokens routed to that expert, after input quantization, as the dense path's Hessian does. - `GPTQHelper` takes an optional `weight_quantizer` and `weight`, gains `accumulate()`, and `update_weights()` writes the result back in place, so an expert's slice is updated inside the 3-D parameter. `GGMLGPTQHelper` pins each expert's codes to its own quantizer. - Native Transformers FP8 experts store weights in 8 bits and hand calibration dequantized copies, so an in-place update would be lost. GPTQ and imatrix now refuse them before any calibration state changes and ask for `unpack_weight()`, as folding already does. - An expert no calibration token reaches has a zero Hessian and gets the plain search, the same fallback a dense layer calibration never reaches already gets. - Each expert Hessian is `in_features²` float32: 22.5 GiB per MoE layer for GLM-5.3-Flash's experts and 13.3 GiB for Qwen3.8-Flash-Next's, 945 and 638 GiB for the whole model. Large MoEs therefore need layerwise calibration, which the IQ recipes already enable; `gptq()` reports the size and warns before allocating when it exceeds the free memory of the GPUs holding the experts. - Behavior change: the built-in IQ1/IQ2 recipes (`[gptq (layerwise), imatrix]`) now run GPTQ on the fused experts too, and imatrix then only encodes weights GPTQ leaves. The `<format>_experts_only` recipes stay imatrix-only. - Recipe comments, `ptq.md`, config docstrings and the changelog no longer say GPTQ skips the experts. The toy fused-MoE test model moved to `_test_utils` so the GPTQ and imatrix tests share it. ### Usage ```python import modelopt.torch.quantization as mtq from modelopt.recipe import load_recipe # [gptq (layerwise), imatrix]: fused experts now get per-expert GPTQ. config = load_recipe("general/ptq/iq2_xxs").quantize.model_dump() mtq.quantize(moe_model, config, forward_loop) ``` ### Testing - `test_gptq_quantizes_each_fused_expert_as_a_linear_fed_only_its_tokens`, run with IQ2_XXS (`GGMLGPTQHelper`) and INT4 (the base `GPTQHelper`): every expert's result, both projections, is bit-identical to dense GPTQ on an `nn.Linear` holding that expert's weight and fed only that expert's tokens; for IQ2_XXS the pinned GGML payload matches too. At the toy width a GGML weight is a single 256-column group, whose GPTQ encode does not use the Hessian, so the INT4 case is the one that checks routing: it fails if every token goes to expert 0's Hessian. Both fail on `main`. - `test_layerwise_gptq_calibrates_fused_experts_on_the_earlier_layers_output`: on a two-layer toy MoE, layerwise GPTQ writes every layer's experts, layer 0 matches a single pass, and layer 1 is calibrated on layer 0's quantized output. 3.8 s on CPU. - `test_native_fp8_experts_reject_packed_weight_updates` (GPTQ and imatrix): packed FP8 experts are refused with the state untouched, and the updates persist after `unpack_weight()`. Fails without the guard. - `tests/unit/torch/quantization/{test_ggml_gptq,test_ggml_imatrix,test_gptq,test_layerwise_calibrate,test_calib}.py`, `plugins/test_fused_experts.py` and `tests/unit/recipe` on CPU with transformers 5.18: 761 passed at the top of the stack. - Real MoEs on 4x GB300 with #2864 and #2877 on top: layerwise GPTQ on the routed experts of Qwen3.8-Flash-Next and GLM-5.3-Flash at IQ2_XXS, IQ2_XS and IQ2_S, including Qwen's NVFP4 `down_proj` experts through the fused Triton kernel. KL to BF16 and downstream accuracy against imatrix are in the comments. - Not run: the GPU unit tests (no local GPU). ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ (new optional arguments; dense GPTQ writes its result back with `copy_` instead of replacing `.data`) - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ - Did you get Claude approval on this PR?: ❌ ### Additional Information Follows #2735 (imatrix calibration for the GGML formats). 🤖 Generated with [Claude Code](https://claude.com/claude-code)   ## Summary by CodeRabbit * **New Features** * GPTQ calibration now supports fused Mixture-of-Experts layers using routed tokens and per-expert calibration. * Experts receiving no routed tokens are handled with a fallback calibration path. * Imatrix calibration applies to weights GPTQ leaves unquantized while preserving already encoded weights. * Layerwise calibration helps limit Hessian memory use for large MoE models. * **Bug Fixes** * Calibration now safely rejects unsupported packed native FP8 expert weights. * **Documentation** * Updated IQ quantization recipe guidance for expert-level GPTQ and imatrix coverage.  --------- Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

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Nudge: the optimizations look sound, but the broader layerwise batch default and offload safety paths need regression coverage.

Needs action:

Add an hf_ptq regression exercising dense layerwise calibration with automatic batching; cover the sample-count cap, long sequences, and explicit --batch_size 1 preservation.
Extend test_hessian_of_a_full_gpu_goes_to_an_idle_gpu to verify CPU fallback when eligible GPUs are full and that multi-rank runs never select another GPU.

No action needed:

The design extends existing GPTQ, max-calibration, and PyTorch memory utilities; the PR explains why CPU-only offload and full-model probing are unsuitable here.
Existing tests retain their assertions; the forward-count and cross-device Hessian tests cover the primary optimizations. Tests were inspected, not executed.

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Inline comments:
Review comments at @docs/source/announcements/20260928-single-gpu-ptq.rst:
- Around line 136-137: Update the layerwise batch-size description near the
`hf_ptq.py` reference to describe its default as calculated from an 8,192-token
budget, not determined by an auto-probe. State that users may need to set a
smaller batch size explicitly for their GPU.

Review comments at @modelopt/torch/quantization/utils/calib_utils.py:
- Around line 65-68: Update _hessian_offload_device to accept the Hessian’s
required allocation size and reject GPU candidates whose available free memory
is insufficient, rather than selecting them based only on used-memory fraction.
Preserve the existing CPU fallback when no GPU has enough capacity.

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CHANGELOG.rst
docs/source/announcements/20260928-single-gpu-ptq.rst
examples/hf_ptq/hf_ptq.py
modelopt/torch/quantization/model_calib.py
modelopt/torch/quantization/utils/calib_utils.py
tests/gpu/torch/quantization/test_gptq.py
tests/unit/torch/quantization/test_gptq.py

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cjluo-nv · 2026-10-22T04:34:25Z

Follow-up to the bot review, in 34a1cd8:

CPU fallback and multi-rank: test_hessian_of_a_full_gpu_falls_back_to_the_cpu sends the Hessian to the CPU in three cases: the idle GPU is full, the idle GPU lacks the Hessian's bytes (the new capacity check from CodeRabbit's comment), and any rank of a multi-rank run. CI's GPU runner has one GPU and skips these 2-GPU tests, so I ran them on 2x RTX PRO 6000. All of tests/gpu/torch/quantization/test_gptq.py passes: 14 tests here, and 15 with Launch the fused NVFP4 GPTQ kernel on its tensors' GPU #2877 on top.
hf_ptq default batch: the bound now lives in _layerwise_calib_batch_size(). test_layerwise_default_batch_size pins its token budget (16 at 512 tokens), its sample-count cap, and its floor of 1 for sequences over 8,192 tokens. An explicit --batch_size, including 1, never reaches this default: it only applies under if args.batch_size == 0, which this PR leaves unchanged. I didn't add an end-to-end dense layerwise hf_ptq run. It would load and calibrate a model just to exercise that one bound, which the unit test now covers.

meenchen

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Nudge: Hessian safety concerns are resolved, but the batch-default tests still do not exercise the hf_ptq caller or explicit batch preservation.

Needs action:

💬 Author added helper tests and declined a full dense run — add a lightweight quantize_main regression in test_hf_ptq_args.py capturing the dataloader batch size for automatic batching and explicit --batch_size 1; no checkpoint download is needed.

No action needed:

✔️ Resolved since the last review: CPU fallback and multi-rank coverage, Hessian capacity checks, token-budget bounds, and the misleading auto-probe documentation.
The design extends existing GPTQ and PyTorch memory utilities; the PR justifies avoiding CPU-only offload and full-model probing.
Tests were inspected, not executed; existing assertions remain intact.

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Review comments at @examples/hf_ptq/hf_ptq.py:
- Line 119: Validate calib_seq immediately after argument parsing and reject
values less than or equal to zero with a parser error, before the layerwise
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🟠 Major · Preserve the materialization forward for DeepSpeed ZeRO-3. · model_calib.py:2360

modelopt/torch/quantization/model_calib.py:2360
🗄️ Data Integrity & Integration | 🟠 Major | 🏗️ Heavy lift

Preserve the materialization forward for DeepSpeed ZeRO-3.

When activation calibration is unnecessary, this call skips forward_loop. MaxCalibConfig documents that forward_loop can materialize sharded parameters under ZeRO-3. Weight calibration then accesses parameters through a helper with no DeepSpeed gather path. A weight-only GPTQ run under ZeRO-3 can therefore update local parameter shards instead of gathered weights. Keep the skip disabled for ZeRO-3, or add explicit gather and writeback handling.
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Review comment at @modelopt/torch/quantization/model_calib.py at line 2360:
Update the max_calibrate call so skip_forward_without_activation_calib does not
skip forward_loop when running under DeepSpeed ZeRO-3, preserving parameter
materialization before weight calibration; retain the skip behavior for other
configurations.

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Review comments at @modelopt/torch/quantization/model_calib.py:
- Line 2360: Update the max_calibrate call so
skip_forward_without_activation_calib does not skip forward_loop when running
under DeepSpeed ZeRO-3, preserving parameter materialization before weight
calibration; retain the skip behavior for other configurations.

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modelopt/torch/quantization/model_calib.py
modelopt/torch/quantization/utils/calib_utils.py

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meenchen

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Changes requested: the caller-level batching regression remains missing, and non-positive --calib_seq still reaches the new division unchecked.

Needs action:

Fix --calib_seq validation in examples/hf_ptq/hf_ptq.py; reject zero and negative values during parsing and add regression tests.
💬 Author added helper tests and declined a full dense run — add the lightweight quantize_main regression in test_hf_ptq_args.py, capturing automatic and explicit batch-1 dataloader sizes without downloading a checkpoint.
Confirm ZeRO-3 compatibility of the skipped max forward in model_calib.py with a regression or a documented unsupported-path explanation; the previous materialization concern remains unsettled.

No action needed:

✔️ Resolved: CPU fallback, multi-rank exclusion, Hessian capacity checks, token-budget bounds, and misleading auto-probe documentation.
Design review: existing max/imatrix calibration and PyTorch memory/GEMM utilities are reused. The body reasonably rejects CPU-only placement and full-model batch probing; no second subsystem is introduced.
Tests were inspected, not executed; existing assertions remain intact.

meenchen · 2026-10-22T15:48:36Z

+
+def _layerwise_calib_batch_size(calib_seq: int, num_samples: int) -> int:
+    """Default layerwise batch size: LAYERWISE_CALIB_TOKENS tokens, but 1 to all of the samples."""
+    return max(1, min(LAYERWISE_CALIB_TOKENS // calib_seq, num_samples))


Bot comment.

The previous non-positive calib_seq finding is still present: parse_args() accepts zero, and default layerwise batching evaluates 8192 // 0 before constructing the dataset. Reject args.calib_seq <= 0 with parser.error immediately after parsing, and test zero and negative inputs.

meenchen · 2026-10-22T15:48:36Z

+    ids=["token_budget", "sample_cap", "long_sequence"],
+)
+def test_layerwise_default_batch_size(monkeypatch, calib_seq, num_samples, expected):
+    hf_ptq = _import_hf_ptq(monkeypatch)


Bot comment.

This still tests only the arithmetic helper, not the caller-level regression requested in the last review. Please exercise quantize_main with a dense layerwise recipe and capture the batch size passed to make_calib_dataloader for automatic batching and explicit --batch_size 1. Stub downstream calibration/preview/export or stop at the captured call; no checkpoint download or full dense calibration run is necessary.

meenchen · 2026-10-22T15:48:36Z

    # TODO: Add support for other scale setting strateiges like weight-mse or local-hessian
-    max_calibrate(model, forward_loop=forward_loop)
+    # The Hessian pass below is GPTQ's own forward; max only runs one if activations need amax.
+    max_calibrate(model, forward_loop=forward_loop, skip_forward_without_activation_calib=True)


Bot comment.

The prior ZeRO-3 materialization finding needs an explicit disposition. enable_weight_access_and_writeback handles FSDP2, TP and accelerate but has no explicit DeepSpeed gather branch. However, max_calibrate already accesses weights before its optional forward, so the earlier claim that retaining that forward alone fixes this is not established by the current code. Please verify the supported ZeRO-3 GPTQ path with a regression, or document why this combination is unsupported/pre-existing rather than silently assuming the skipped forward is safe.

cjluo-nv · 2026-10-22T16:15:27Z

Follow-up to the latest bot review:

--calib_seq: 024bc1e makes hf_ptq.py reject a zero or negative --calib_seq while parsing arguments (parser.error, next to the other range checks). test_calib_seq_must_be_positive covers it.
Caller-level batching: b60598a adds test_layerwise_calibration_batch_reaches_the_dataloader. It drives quantize_main with the layerwise general/ptq/iq2_xxs recipe up to make_calib_dataloader, without a checkpoint, and checks the batch the dataloader receives in four cases:
- 16 at 8,192 tokens
- capped at the sample count
- 1 for sequences over 8,192 tokens
- an explicit --batch_size 1 kept as is
It fails with the old batch-1 default and replaces the helper-only test.
ZeRO-3: skipping this forward doesn't change GPTQ's behavior under ZeRO-3.
- max_calibrate calibrates weight amax in weight_only_quantize() before it would run the forward, so the forward never fed weight calibration.
- GPTQ then runs its own Hessian forward regardless.
- Its weight update reads the weights through enable_weight_access_and_writeback(), which has gather paths for FSDP2, TP-sharded linears and accelerate offload, but none for DeepSpeed.
So GPTQ on a ZeRO-3-partitioned model wasn't supported before this PR either, and an extra max forward would not change that. imatrix on main already passes the same skip_forward_without_activation_calib=True. Supporting ZeRO-3 in GPTQ, or refusing it explicitly, would be a separate change.

meenchen

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Approved: the caller-level batching regression and sequence validation are fixed, and the ZeRO-3 reply establishes the materialization concern as pre-existing.

No action needed:

✔️ Resolved since the last review: batching coverage, non-positive calib_seq handling, and ZeRO-3 disposition; earlier Hessian safety fixes remain intact.
The design reuses existing calibration and PyTorch utilities, with reasonable alternatives addressed in the PR body.
Tests were inspected, not executed. Existing assertions remain intact.

Large PR: spans 5 directories (≥ 5). The review came back clean, so this is an LGTM — a human should take the final look and approve.

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Review comments at @examples/hf_ptq/hf_ptq.py:
- Line 1003: Update the layerwise calibration setup around
_layerwise_calib_batch_size to keep the default batch size at 1 instead of
automatically assigning the computed 16-sequence batch; make the larger computed
batch opt-in, while preserving an explicitly supplied --batch_size.

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coderabbitai · 2026-10-22T20:25:15Z

        elif is_layerwise:
-            print("Layerwise calibration enabled. Using default batch_size=1 for calibration.")
-            args.batch_size = 1
+            args.batch_size = _layerwise_calib_batch_size(args.calib_seq, sum(args.calib_size))


🩺 Stability & Availability | 🟠 Major | 🏗️ Heavy lift

🔎 Supported by static analysis

🏁 Script executed:

rg -n -F -e '_layerwise_calib_batch_size' -e 'LAYERWISE_CALIB_TOKENS' -- examples/hf_ptq/hf_ptq.py || test "$?" -eq 1 echo '--- helper and call site ---' sed -n '1,5p' /dev/null ast-grep run --lang python --pattern 'def _layerwise_calib_batch_size($$$): $$$' examples/hf_ptq/hf_ptq.py echo '--- OOM handling in quantization/calibration code ---' rg -n -i -e 'OutOfMemoryError' -e 'out of memory' -e 'OOM' -e 'torch.cuda.OutOfMemory' -- modelopt/torch/quantization examples/hf_ptq || test "$?" -eq 1 echo '--- layerwise calibration entry points ---' rg -n -F -e 'layerwise' -- modelopt/torch/quantization/model_calib.py modelopt/torch/quantization/algorithms.py || test "$?" -eq 1

Repository: NVIDIA/Model-Optimizer

Length of output: 11162

Guard the new layerwise default batch against OOM.

At the default --calib_seq 512, the default layerwise batch is now 16 sequences, or 8,192 tokens per forward pass. The previous default was 1. If a layer fits one sequence but not 16, calibration can run out of memory. Layerwise calibration has no OOM retry, so the run can stop. The explicit --batch_size 1 flag restores the previous behavior.

Choose one of these changes:

Cap the default batch size by available GPU memory.

Keep batch size 1 as the default. Make the 8,192-token batch an opt-in setting.

A retry with a halved batch after an OOM error is also possible. It requires more change than the two options above.

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Fridah-nv · 2026-10-22T21:31:28Z

        elif is_layerwise:
-            print("Layerwise calibration enabled. Using default batch_size=1 for calibration.")
-            args.batch_size = 1
+            args.batch_size = _layerwise_calib_batch_size(args.calib_seq, sum(args.calib_size))


What's the reasoning behind to change the default BS for layerwise? For some algo like local-hessian we recommend BS=1 now to avoid data padding and ensure correctness.

Maybe we can just set --batch_size flag explicitly for a specific experiment setting?

Thanks. The reason is speed on HF fused-MoE models. Their experts run a Python loop over experts on every forward, so a forward costs about the same per call whatever the batch: on a Qwen3.8-Flash-Next-shaped layer, the calibration forward over 32 × 512 tokens takes 8.9 s at batch 1 and 1.0 s at batch 16. Batch 1 was the layerwise default only because the auto-probe runs a full-model forward. A layerwise batch holds one layer's activations, so the default is now 8,192 tokens per batch. An explicit --batch_size still wins, so the local-Hessian blog command (--batch_size 1) is unchanged.

On padding, batch size makes no difference in hf_ptq.py. get_dataset_dataloader tokenizes all calibration samples in one tokenizer(..., padding=True, truncation=True, max_length=calib_seq) call, so every row is padded to the same length before batching. Neither the forward loop nor the calibration code trims or masks pad positions. Measured at batch 1 and 16, with identical per-row pad counts:

Calibration set Rows × length Pad tokens

cnn_nemotron_v2_mix (hf_ptq default) 128 × 512 5.7%

nemotron-post-training-v3 (blog command) 140 × 2,048 34.0%

So batch 1 doesn't keep pad tokens out of the statistics today. That needs per-batch padding, packing, or masking pad positions in the Hessians; I can look at that in a separate PR.

Results change with batch size only through floating-point rounding; two batch-1 runs are bit-identical. phi-2, NVFP4 W4A4, layerwise, 128 × 512 cnn_dailymail calibration; KL to BF16 on wikitext-2 (40 × 512 tokens, second half scored), difference paired per chunk (± one standard error):

Batch 1 Batch 16 16 − 1

GPTQ 0.1427 0.1369 −0.0058 ± 0.0032

Local-Hessian (fp8_scale_sweep) 0.1281 0.1339 +0.0058 ± 0.0052

Neither difference is significant, and they go in opposite directions.

I'd like to keep the token-budget default so large MoE runs aren't slow by default. If you'd still rather keep 1, I'll revert it and pass --batch_size in the commands we publish.

Thanks for the detailed explanation! I agree with your point about padding in the current dataloader implementation.

My main concern is that the fixed 8K-token budget may not generalize well across different models and calibration settings, and could potentially lead to OOM for some workloads.

Just something to consider for the default behavior. I wonder if a more general solution would be a layerwise-aware batch size probe, similar to what we do for general HF PTQ, but probing a representative layer instead of the full model. That might help balance performance and memory constraints across different workloads.

Yes I have PR #2945 to address this

Correction: #2945 doesn't change the batch size. It keeps 1.5x the measured calibration-forward peak free when placing Hessians, so Hessians can't crowd out the forward, but a layer whose 8K-token forward doesn't fit will still OOM. A layerwise-aware batch-size probe on a representative layer is a separate change; I'll open a follow-up for it.

The layerwise batch probe is now in #2945 (21c8b94). With --batch_size left at its default, hf_ptq.py runs the first decoder layer of each kind on one and two samples and keeps the largest batch whose forward fits in half of the GPU's available memory, after the activations layerwise calibration caches. The 8K-token budget stays the cap. On 4x GB300, GLM-5.3-Flash gets 16 even without the cap (it ran out of memory at 64), and Qwen3.8-Flash-Next fits 177, so it keeps 16.

### What does this PR do? Type of change: Bug fix Standalone on `main`, split out of the GPTQ-for-MoE stack (#2840 merged ← #2864 ← #2945). It touches only the fused kernel, so it can merge in any order with that stack. GPTQ with `fused: true` runs the NVFP4 column update in a Triton kernel (`gptq_fused_block_scalar`, in modelopt since 0.45). Triton launches on the **current** CUDA device, and the wrapper launched without selecting the device of the tensors it was given. A model that `device_map="auto"` spreads over several GPUs keeps later decoder layers off the current GPU. Once layerwise GPTQ reached such a layer, the kernel ran on GPU 0 with pointers into GPU 1: - **NVLink systems (GB300):** `CUDA error: an illegal memory access was encountered`, reported later at an unrelated op. - **PCIe systems:** `ValueError: Pointer argument cannot be accessed from Triton`. The fix launches the kernel under `torch.cuda.device(w_block.device)`, as the other quantization Triton wrappers (`fp4_kernel.py`, `nvfp4_fp8_sweep.py`, `bmm2_qdq.py`) already do. Found while running layerwise GPTQ on Qwen3.8-Flash-Next with the experts' `down_proj` in static NVFP4 (`fused: true`). The model sits on 4 x GB300 with layers 0-22 on GPU 0. Three runs on three different nodes all died right after layer 22. ### Usage No API change. ### Testing - **New GPU test** `test_fused_nvfp4_gptq_runs_on_the_weights_gpu` (2 GPUs): the fused update with every tensor on `cuda:1` and `cuda:0` current matches the same update run on `cuda:0` bit for bit. It fails without the fix with the Triton pointer error on the PCIe GPUs I ran it on. - **Existing tests:** on `main`, `tests/gpu/torch/quantization/test_gptq.py` passes on 2x RTX PRO 6000 (11 tests, including the existing fused-vs-unfused NVFP4 ones). - **End to end:** the Qwen3.8-Flash-Next layerwise GPTQ that died after layer 22 completes with this change on oci-jhb; details in the comment below. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ (bug present since 0.45) - Did you get Claude approval on this PR?: ❌ 🤖 Generated with [Claude Code](https://claude.com/claude-code)   ## Summary by CodeRabbit * **Bug Fixes** * Fixed GPTQ fused quantization failing with CUDA or Triton errors when calibration runs across multiple GPUs. * Added a check that quantization results remain consistent when processing data on a different GPU than the current device.  Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

Fridah-nv

LGTM

GPTQ ran a max-calibration forward before its own Hessian forward even when no activation quantizer needed it; weight amax comes from the weights. Skip it, as max and imatrix already do. Fold the Hessian's incremental average into one addmm, which runs once per expert per forward on HF fused-MoE layers. A Hessian whose GPU was over 65% used went to the CPU, so every update copied the whole matrix to the host and rescaled it there. A model sharded over several GPUs fills the one holding the current layer while others sit idle; a single process now puts the Hessian on the emptiest GPU it already uses that is under the limit, and the CPU only when none is. hf_ptq's layerwise default batch size becomes a token budget (8192 tokens, 16 x 512) instead of 1: a batch holds one layer's activations, and batch 1 made each MoE layer run its per-expert loop once per calibration sample. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

- A Hessian leaving a full GPU now goes only to a GPU that is under the usage limit and has the Hessian's bytes free, else to the CPU; a GPU that passes the usage limit but is too small would otherwise OOM. - Test the CPU fallback on two GPUs: the idle GPU full, the idle GPU too small, and any rank of a multi-rank run. - Move hf_ptq's default layerwise batch size into a helper and test its token budget, sample cap and long-sequence floor. - Describe that default in the single-GPU PTQ note as a token budget, not a memory probe. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

Fused-expert GPTQ helpers no longer log a Hessian-weighted error each: it cost two weight-by-Hessian matmuls and a host sync per expert. gptq() logs one line per experts module instead. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

The default layerwise batch divides the token budget by --calib_seq, so 0 raised ZeroDivisionError; argument parsing now rejects it. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

Drive quantize_main with a layerwise recipe up to the calibration dataloader and check the batch it gets: the 8,192-token default, its sample cap and floor, and an explicit --batch_size kept as is. This replaces the helper-only test. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

…e and offload fixes (#2945) Follow-up to #2864 (merged). Four commits, all about the GPU memory and speed of layerwise GPTQ calibration: 1. **Place GPTQ Hessians by measured headroom** instead of a 65% usage cap. 2. **Size the default layerwise calibration batch by probing each kind of decoder layer.** Fridah-nv raised this on #2864: its fixed 8,192-token default could run out of memory on a model or GPU whose decoder-layer forward is larger than the ones it was tuned on. 3. **Raise the layerwise token cap from 8,192 to 32,768 tokens**, now that the probe keeps the batch within memory. A model whose layers fit the larger batch runs its MoE layers' per-expert loops a quarter as often. 4. **Keep offloaded layers' quantizer state off the GPU, and stop layerwise OOM retries.** Two bugs in calibrating a CPU- or disk-offloaded model, found running the first three commits on a single L40S. The first two fit together. The batch probe leaves half of a GPU's available memory for quantizers and Hessians, and the Hessian placement keeps room for the measured calibration forward. ### What does this PR do? Type of change: Bug fix (performance), new feature #### 1. Hessian placement by measured headroom `GPTQHelper` kept a Hessian on a GPU only while the GPU was under 65% used (`_HESSIAN_GPU_MEM_LIMIT`, inherited from GPTQ Lite). The rule exists so the calibration forward that follows still has room. But a fraction of the card is a poor proxy for that: - on a 284 GB GB300 it reserves about 100 GB, far more than a layer's forward needs; - the reading counts PyTorch's cached-but-unused blocks as used; - each Hessian is decided separately, so one layer's Hessians can end up split between a GPU and the CPU. On GLM-5.3-Flash over 4x GB300 (layerwise GPTQ on all experts, IQ2_XXS), every GPU the process used passed 65% halfway through the model. From then on each MoE layer's Hessians went to the CPU and the layer took 142 s instead of 43 s: 21 of 42 MoE layers, 34.5 extra minutes of an 83-minute calibration. Each GPU still had 90-100 GB free. A GPU now takes a Hessian if, after allocating it, the memory available to an allocation still covers a reserve. Available means the driver's free memory plus this process's cached blocks, minus the free pieces of partly used blocks, which a large allocation cannot use. The reserve is 1.5x the most a GPTQ calibration forward has added on that GPU, and at least 10% of it. - The forward's need is measured around `gptq()`'s Hessian forward as peak minus allocated-before. PyTorch's peak counters are reset first. Otherwise an earlier peak, such as the previous layer's Hessians and GPTQ updates, would count as forward memory and send Hessians to the CPU for nothing. - Until a forward has been measured on a GPU (the first layer, and every non-layerwise run), the reserve is 35% of the GPU, which keeps today's behavior. - Each calibration run measures afresh, so a run on another model never inherits a smaller reserve. - The `Computing Hessians ...` log line now says where the Hessians went. #### 2. Layerwise batch probe `get_max_layerwise_batch_size` (in `modelopt/torch/quantization/utils/layerwise_calib.py`) probes the batch the way `get_max_batch_size` does for full-model calibration, but per decoder layer: - It runs the first decoder layer of each kind on one and two dummy samples. Layers are grouped by module classes and parameter shapes: dense or MoE, full or linear attention, and per-layer widths such as Nemotron-NAS's. The growth of its peak memory on each GPU is that GPU's cost per sample. Every other GPU that runs a layer of that kind is charged the same cost, so with a `device_map` over several GPUs, the GPU with the least headroom sets the batch. A warm-up forward first keeps one-off buffers such as cuBLAS's workspace out of the measurement. - The dummy rows carry one left-pad token, so the model builds the explicit attention mask that padded calibration batches get. - A batch fits when the layer's forward stays within half of the GPU's available memory, after the inputs layerwise calibration caches for every sample (this layer's and the next). - A layer that fits one sample but runs out of memory on two gives a batch of one, rather than stopping the run. - A token budget caps the batch: 32,768 tokens (64 samples at `--calib_seq 512`) after the third commit, up from #2864's 8,192. - All ranks return the smallest batch over ranks, so FSDP2 ranks keep identical dataloaders. `hf_ptq.py` calls it only when `--batch_size` is left at its default; an explicit value passes straight through. The second commit also corrects two statements about padding. `hf_ptq.py` pads every calibration sample to one length before batching, so batch size doesn't change which tokens are padding. #2864's changelog entry and the single-GPU PTQ blog note said otherwise. #### 4. Offloaded calibration **Quantizer state of offloaded layers piled up on the GPU.** Accelerate's offload hooks only move the tensors a module had when it was dispatched. The state calibration adds afterwards stayed on the execution device for every offloaded layer: NVFP4's per-block `_amax` (about 0.25 bytes per weight), its calibrator's copy, and GGML payload pins. The first time a module's weights are materialized for weight access, its offload hook now also carries its quantizers' state. A forward gets GPU copies; afterwards the CPU tensors are the master copy again. A quantizer's own tensors are always copied back into that master first, because its setters (`amax`, `pre_quant_scale`, `bias_value`, `global_amax`) write through `.data` and leave `_version` unchanged. For a Qwen3-30B-A3B MoE layer's per-block `_amax` that copy takes 9-11 ms, against about 60 ms to onload the layer. The calibrators' tensors (the amax calibrator's and, under static bias, the bias calibrator's) are copied back only when the forward replaced or modified them. Tensors the hook already manages, such as buffers under a hook that places submodules, are left alone. GGML's packed-weight cache is keyed to the weight that was just released, so it is dropped with it. **A GPU OOM in a layerwise capture forward made it replay the wrong activations.** The calibration forward loop catches an OOM, splits the data batch and retries. In a layerwise capture forward, the only real work is the previous layer, in 'run' mode, replaying the batches it cached, one per call and at their cached size. The retry cannot shrink that work, and each retried call replays the *next* cached batch. The captured activations then no longer match the data batch, or the cache runs out first (`AssertionError: Layer model.layers.43 is in 'run' mode but has no cached inputs to replay`). An OOM there now stops with an error asking for a smaller calibration batch size. ### Usage No API change and no new flag. With a layerwise recipe and no `--batch_size`, `hf_ptq.py` now prints the probed batch: ```text Layerwise calibration enabled. Using batch_size=55, the most that fits a decoder layer's forward, up to 64 (32768 tokens). ``` ### Testing **Hessian placement on 4x GB300.** Layerwise GPTQ on the routed experts at IQ2_XXS, batch 16, 1,024 x 512 calibration tokens, KL to BF16 over wikitext-2 (100 x 512 tokens, second half scored). "Before" is #2864 at 34a1cd8. "After" is the first commit, which also includes #2864's later per-expert-log commit, worth about 2 s per MoE layer. | | Before (65% cap) | After | |---|---|---| | GLM-5.3-Flash layerwise calibration | 83.0 min | **42.2 min (-49%)** | | GLM-5.3-Flash GPTQ per MoE layer | 43 s, but 142 s for the last 21 of 42 | 34-44 s for all 42 | | Qwen3.8-Flash-Next layerwise calibration | 47.8 min | 45.7 min (-4%) | | Qwen3.8-Flash-Next GPTQ per layer | 30-37 s | 29-33 s | | KL to BF16, GLM / Qwen | 0.6232 / 0.2492 | 0.6192 / 0.2539 | - **GLM placement:** in the GLM run, every layer's Hessians stayed on GPUs and none went to the CPU. For 31 of 42 layers they all sat on one GPU. For the other 11, part went to an idle GPU. No layer ran out of memory. - **KL:** the changes are not significant, paired per chunk: GLM -0.0040 +- 0.0050 (p 0.43), Qwen +0.0047 +- 0.0031 (p 0.14). They come from summing Hessians on a GPU rather than the CPU, which rounds differently. **Batch probe on the same models and GPUs** (second commit, cap 16), run up to the probe and stopped before calibration, also without the cap: | Model | Fit per layer kind | Probed batch (cap 16) | Uncapped | Probe time | |---|---|---|---|---| | GLM-5.3-Flash | linear attn + MLP: 16; full attn + MoE: 245; linear attn + MoE: 16 | 16 | 16 | 37 s | | Qwen3.8-Flash-Next | 190 / 177 (linear attn + MoE); full attn + MoE: 236 | 16 | 177 | 45 s | GLM-5.3-Flash previously ran out of memory at batch 64 in its linear attention (`chunk_kimi_delta_attention`) and ran fine at 16. Uncapped, the probe picks 16. Both models get batch 16, the batch the timings above used. **Token cap 32,768 (third commit) on the same models and GPUs**, full layerwise GPTQ, KL paired per chunk against the runs above: | | Probed batch | Layerwise calibration | KL to BF16 | |---|---|---|---| | GLM-5.3-Flash | 16 (memory) | 42.2 -> 41.2 min | 0.6192 -> 0.6197 (+0.0005 +- 0.0033, p 0.87) | | Qwen3.8-Flash-Next | 64 (cap) | 45.7 -> **21.1 min (-54%)** | 0.2539 -> 0.2493 (-0.0046 +- 0.0026, p 0.08) | **Smaller GPUs (computelab), single GPU, all three commits:** `hf_ptq.py` with layerwise NVFP4 GPTQ (`general/ptq/nvfp4_default-kv_none-gptq`), 512 x 512 cnn_dailymail calibration tokens, default `--batch_size`. GPU memory sampled every 2 s with `nvidia-smi`. | GPU | Model | Placement | Probed batch | Result | Peak GPU memory | |---|---|---|---|---|---| | H100 NVL 94 GB | Qwen3.6-35B-A3B | whole model on the GPU | 55 | completed, 40 layers | 89.5 GB | | H100 NVL 94 GB | Qwen3.6-35B-A3B | `--max_gpu_memory_gb 40`, rest on CPU | 64 | completed | 63.4 GB | | L40S 46 GB | Qwen3-30B-A3B | `--max_gpu_memory_gb 30`, per-layer export | 64 | completed, 48 layers in 1 h 40 min | 35.4 GB | | L40S 46 GB | Qwen3.6-35B-A3B | `--max_gpu_memory_gb 30`, per-layer export | 64 | completed, 40 layers in 2 h 31 min | 38.8 GB | **Offloaded without per-layer export (fourth commit).** Without `layerwise.export_dir`, every finished offloaded layer's quantizer state stayed on the GPU. On the L40S with a 30 GB weight budget that grew by about 8 GB over the model and ran out of memory near the end, even after splitting the batch down to one sample. With the fourth commit, same L40S, no per-layer export, default batch size: | Model | Before | With the fourth commit (batch 16) | |---|---|---| | Qwen3-30B-A3B (48 layers) | OOM at layer 44 (batch 64), peak 45.1 GB | completed in 1 h 56 min, peak 42.4 GB | | Qwen3.6-35B-A3B (40 layers) | OOM at layer 34 (batch 29, then split down to one sample), peak 45.5 GB | completed in 2 h 56 min, peak 43.9 GB | GPU memory still rises while the layers that `device_map` keeps on the GPU are calibrated, since their quantizer state belongs there. It stays flat over the offloaded layers, then rises during export. On phi-2 with CPU offload (`--max_gpu_memory_gb 2`), memory allocated during calibration grew about 30 MiB per offloaded layer before and is now flat, ending at 2,075 MiB instead of 2,723. `hf_ptq.py`'s export there is bit-identical to `main`'s, every tensor, and so are its generations before and after PTQ, with layerwise calibration time unchanged (9 min 44 s vs 10 min 33 s on a shared GPU). - **Linearity (phi-2, RTX PRO 6000):** a decoder layer's forward peak is 45 MiB per sample at 512 tokens and 180 MiB at 2,048. Measured at batches 1, 2, 8 and 32, it matches the probe's prediction exactly. - **Calibration overhead:** during layerwise GPTQ and layerwise local-Hessian on phi-2, the decoder layer's forward peaks at 1.18x the plain forward the probe measures (106 vs 90 MiB at batch 2), well inside the half the probe leaves free. - **End to end:** `hf_ptq.py` with layerwise GPTQ on phi-2, single GPU and `--use_fsdp2` on 2 GPUs: the probe picks 16, calibration runs all 32 layers, and the export completes. - **Ranks agree:** two ranks with different memory (local fits 56 and 14) both return 14. **Tests**, on this branch, 2x RTX PRO 6000 (CI's GPU runner has one GPU and skips the 2-GPU ones): `tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py` 29 passed. The offload and export tests (`tests/gpu/torch/quantization/plugins/test_accelerate_gpu.py`, `tests/gpu/torch/export/{test_offload_export,test_vllm_fakequant_hf_export}.py`, `tests/gpu/torch/utils/test_model_load_utils.py`) also pass: 29 of 30 in one run, where `test_transformers_mse_calibrate_fp32_amax_save_restore[dtype0]` hit the 120 s timeout as the first test on a shared GPU, as it does on `main`; alone it passes in 3 s. CPU: `tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_layerwise_calibrate,test_calib}.py`, `plugins/{test_accelerate,test_huggingface}.py`, `tests/unit/torch/export/test_offload_export.py` and `tests/examples/hf_ptq/test_hf_ptq_args.py` 214 passed. A wider CPU run on the first commit alone (adding `test_ggml_imatrix`, `test_quantize_cpu`, `plugins/test_fused_experts.py` and `tests/unit/recipe`) passed 882. - The two placement tests from #2864 now report available memory instead of a usage fraction. - New `test_a_measured_calibration_forward_sets_the_hessian_reserve`: at 30% available, a GPU keeps a Hessian only once a small forward has been measured. It falls back with no measurement, or with a large one. - New `test_gptq_records_its_calibration_forward_memory`: a forward that allocates 64 MiB raises the recorded need at least that much. - New CPU test `test_a_calibration_run_forgets_earlier_forward_measurements`: a stale measurement does not survive a new `mtq.quantize`. It fails without the reset. - New GPU test `test_layerwise_batch_probe_fits_the_heaviest_kind_of_layer`: a model with a second, heavier kind of decoder layer gets a smaller batch, and that layer's forward at the probed batch stays within half the budget. It fails when only the first layer is probed. - `test_layerwise_calibration_batch_reaches_the_dataloader` gains a case where memory, not the token budget, sets the batch, and follows the new cap. - `test_layerwise_calibrate_cpu_offloaded` now also checks that the offloaded layer's quantizer and calibrator state is on the CPU between uses. It fails without the fourth commit; its existing comparison with a GPU-only reference still passes. - New CPU test `test_out_of_memory_in_a_replayed_layer_is_not_split`: an OOM while a layer replays its cached batches, under the real calibration forward loop, raises instead of retrying. It fails without the fourth commit with the `AssertionError` above. - **Review fixes (fifth commit)**, each with a test that fails without it: - `test_offloaded_quantizer_keeps_what_its_setter_wrote` (GPU): an offloaded weight quantizer's CPU `_amax`, set through its setter while the layer is materialized, keeps the new value. - `test_layerwise_batch_probe_falls_back_to_one_sample` (GPU): a layer that runs out of memory on two samples, in its forward or while its inputs are captured, gives a batch of one. - `test_gptq_records_its_calibration_forward_memory` now also allocates a 1 GiB peak before GPTQ and checks that it is not counted. - `test_layerwise_batch_probe_fits_the_heaviest_kind_of_layer` gains a case where the heavier layer has the same class but a wider FFN. - On 2x RTX PRO 6000: `tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py` and `plugins/test_accelerate_gpu.py` 50 passed. `tests/unit/torch/utils` 385 passed. In `tests/unit/torch/quantization`, one test fails, `test_utils.py::test_persistent_materialization_only_skips_unshard_for_its_layer[True]`, and it fails on the previous head too. Two other modules there need optional packages this environment lacks. - **Second review round (sixth commit)**, each with a test that fails without it: - `test_layerwise_batch_probe_fits_every_gpu_a_kind_of_layer_runs_on` (2 GPUs): two identical layers on cuda:0 and cuda:1, with less memory on cuda:1. The batch is what cuda:1 fits (28, against 113 when only cuda:0 counts). - `test_available_gpu_memory_is_what_one_allocation_can_get` (GPU): after fragmenting the cache, a single allocation of everything reported beyond the driver's free memory comes from the cache without reserving more. - `test_offloaded_layer_keeps_bias_calibration_off_the_gpu` (GPU): a static-bias input quantizer's bias calibrator is on the CPU after layerwise calibration of an offloaded layer. - On 2x RTX PRO 6000: `tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py` and `plugins/test_accelerate_gpu.py` 53 passed. `tests/unit/torch/quantization/{test_layerwise_calibrate,test_gptq,test_ggml_gptq}.py`, `plugins/test_accelerate.py` and `tests/unit/torch/utils` 470 passed. - Not run: VLMs, and FSDP2 CPU offload, which does not use accelerate's hooks and is unchanged. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ (no API change; unmeasured runs keep the 35% Hessian reserve; an explicit `--batch_size` is unchanged and the default can only shrink from #2864's) - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ (updated #2864's unreleased entry, and two bug-fix entries for offloaded calibration; the GPU Hessian placement is unreleased, from #2864) - Did you get Claude approval on this PR?: ❌ ### Additional Information The constants (1.5x margin, 10% floor and 35% before measuring for Hessians; half of available memory for the probed batch) are conservative choices, not tuned values. Review thread on #2864: #2864 (comment) 🤖 Generated with [Claude Code](https://claude.com/claude-code)   ## Summary by CodeRabbit - **New Features** - Layerwise calibration automatically selects a batch size based on available GPU memory, up to a 32,768-token limit and the number of available samples. Explicitly configured batch sizes remain unchanged. - GPTQ accounts for calibration memory when placing Hessians and falls back to CPU when GPU memory is insufficient. - **Bug Fixes** - Improved quantizer state handling for CPU- and disk-offloaded layers during calibration. - Calibration replay out-of-memory errors now explain how to retry with a smaller batch size. - **Documentation** - Clarified that calibration samples use fixed-length padding and that activation input scales may vary slightly with batch size.  --------- Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

cjluo-nv mentioned this pull request Oct 17, 2026

[1/3] Run GPTQ on HF fused MoE experts #2840

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cjluo-nv added this pull request to stack #2875 October 19, 2026 18:13

cjluo-nv mentioned this pull request Oct 19, 2026

Launch the fused NVFP4 GPTQ kernel on its tensors' GPU #2877

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cjluo-nv changed the title ~~Speed up layerwise GPTQ on MoE models (stacked on #2840)~~ [2/3] Speed up layerwise GPTQ on MoE models Oct 19, 2026

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Comment thread docs/source/announcements/20260928-single-gpu-ptq.rst Resolved

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cjluo-nv mentioned this pull request Oct 22, 2026

Improve layerwise GPTQ memory and speed: Hessian headroom, batch probe and offload fixes #2945

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cjluo-nv changed the title ~~[2/3] Speed up layerwise GPTQ on MoE models~~ [2/4] Speed up layerwise GPTQ on MoE models Oct 22, 2026

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cjluo-nv added this pull request to stack #2948 October 22, 2026 20:36

cjluo-nv changed the title ~~[2/4] Speed up layerwise GPTQ on MoE models~~ [2/3] Speed up layerwise GPTQ on MoE models Oct 22, 2026

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cjluo-nv and others added 5 commits October 23, 2026 10:58

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This was referenced Oct 24, 2026

[OMNIML-6284] Add a PTQ scale-comparison tool and hf_ptq --deterministic #2995

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Run fused-MoE expert GPTQ updates on all of this process's GPUs #3002

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cjluo-nv mentioned this pull request Oct 25, 2026

Skip Qwen3.8's QSA indexer loop during calibration when it keeps every token #3008

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coderabbitai Bot mentioned this pull request Oct 26, 2026

Add a bfloat16 option for the GPTQ and local-Hessian Hessian GEMMs #3019

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cjluo-nv mentioned this pull request Oct 27, 2026

Skip local Hessian's max-calibration forward when no activation needs it #3042

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Calibration set	Rows × length	Pad tokens
`cnn_nemotron_v2_mix` (hf_ptq default)	128 × 512	5.7%
`nemotron-post-training-v3` (blog command)	140 × 2,048	34.0%

	Batch 1	Batch 16	16 − 1
GPTQ	0.1427	0.1369	−0.0058 ± 0.0032
Local-Hessian (`fp8_scale_sweep`)	0.1281	0.1339	+0.0058 ± 0.0052

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