[namgyu-yun]

What does this PR do?

Type of change: new feature

GPTQ now uses the fused Triton update by default. The unfused loop fake-quantizes the whole weight once per column, so it is about 64x slower per decoder layer while giving the same MSE. Cases the fused kernel cannot handle (NVFP4 4/6 scaling, pre-SM89 GPUs, no Triton) keep the unfused loop.

Usage

algorithm:
  method: gptq
  fused: false  # previous behavior

Testing

One Qwen3-8B decoder layer, nvfp4_default-kv_none-gptq, H100:

	update_weights	relative MSE (7 linears)
unfused	48.5 s	identical to 3 s.f.
fused	0.76 s	identical to 3 s.f.

pytest tests/gpu/torch/quantization/test_gptq.py        → 12 passed
pytest tests/unit/torch/quantization (GPTQ files)       → 47 passed

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• Is this change backward compatible?: ✅ (fused: false restores the old path)
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Additional Information

Touches the same GPTQCalibConfig as draft #2004.

Summary by CodeRabbit

• New Features
  • GPTQ calibration now defaults to fused execution for static NVFP4 weights on supported GPUs (SM89+) when 4/6 scaling is not enabled. Set fused to false to use the unfused behavior.
• Bug Fixes
  • Fused execution is limited to supported configurations; other formats, unsupported GPUs, and NVFP4 weights using 4/6 scaling continue to use the unfused path.
  • Fused calibration now handles irregular weight widths and block sizes, including sizes that are not multiples of the NVFP4 quantization group size.

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Walkthrough

GPTQ now defaults to fused execution. The fused update uses precomputed FP8 block scales for supported static NVFP4 configurations on SM89 or newer. The kernel handles irregular widths, while unsupported cases continue to use the unfused update.

Changes: GPTQ fused execution

Layer / File(s)	Summary
Update the fused scalar kernel modelopt/torch/kernels/quantization/gemm/gptq_fused_kernel.py	The fused scalar kernel now accepts precomputed FP8 block scales. It processes actual columns and masks padded columns. The wrapper rounds the launch size up to a power of two.
Select and validate fused updates modelopt/torch/quantization/utils/calib_utils.py, tests/gpu/torch/quantization/test_gptq.py	_blockwise_update checks quantizer configuration, scaling, Triton availability, CUDA placement, and compute capability before using the fused path. Scale-block counts use ceiling division. GPU tests compare fused and unfused results for irregular widths and check the 4/6 scaling fallback.
Enable the fused option by default modelopt/torch/quantization/config.py, modelopt/torch/quantization/model_calib.py, CHANGELOG.rst	GPTQCalibConfig.fused and gptq now default to True. The changelog describes fused-path conditions and notes that fused: false restores the previous behavior.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant GPTQHelper
  participant FusedSupportCheck
  participant FusedScalarKernel
  GPTQHelper->>FusedSupportCheck: Check quantizer and device eligibility
  FusedSupportCheck-->>GPTQHelper: Select fused or unfused update
  GPTQHelper->>FusedScalarKernel: Pass block scales for eligible weights

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Possibly related PRs

• NVIDIA/Model-Optimizer#2877: Changes the fused kernel wrapper to launch on w_block.device for the path enabled here.
• NVIDIA/Model-Optimizer#2599: Changes the static NVFP4 fake-quantization kernel used by the unfused fallback.
• NVIDIA/Model-Optimizer#2465: Defines and registers the four_over_six configuration identified as an exception to fused execution.

Suggested reviewers: sychen52, shengliangxu, danielkorzekwa

Merge Risk: Moderate · previous review eda4c

GPTQ can fail for supported two-dimensional NVFP4 weight layouts instead of using the unfused path. Restrict fusion to compatible layouts or support their scale shape before merging.

Pre-merge checks: 6

Check name	Status	Explanation
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly and concisely describes the main change: GPTQ now defaults to the fused NVFP4 update.
Docstring Coverage		Docstring coverage is inconclusive: files from earlier reviews still need to be rechecked within the check's analysis limits. Not blocking.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Security Anti-Patterns		PASS. The pull request changes only GPTQ kernel, quantization, test, and changelog files. Added Python lines contain no torch.load(..., weights_only=False), numpy.load/`np.load(..., allow_pickle=T…

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Explanation

PASS. The pull request changes only GPTQ kernel, quantization, test, and changelog files. Added Python lines contain no torch.load(..., weights_only=False), numpy.load/np.load(..., allow_pickle=True), hardcoded trust_remote_code=True, eval(), exec(), or # nosec. No dependency manifest additions were made.

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[namgyu-yun]

cc @jingyu-ml, @realAsma working on this module, could you take a look?

[kevalmorabia97]

Thanks for the contribution! This PR now has merge conflicts with main. Could you update your branch with the latest main and fix the conflicts so we can continue the review?

We've added the waiting for feedback label while we wait for your update.

[namgyu-yun]

Rebased onto latest main. Since GPTQHelper can now own a fused-MoE expert slice, _fused_supported takes the weight being updated instead of reading module.weight. The CHANGELOG entry moved to 0.49.0.

[meenchen]

Bot review (gpt-6.1-sol) — DM the bot to share feedback.

Changes requested: enabling fusion by default exposes unsupported weight shapes and block sizes that currently pass the capability gate.

Needs action:

• Fix _fused_supported/dispatch in calib_utils.py to handle non-power-of-two GPTQ blocks and final tails, or fall back; add public-API regressions (see inline comment).
• Fix fused dispatch for padded NVFP4 weights in calib_utils.py, or fall back before reshaping scales; add a non-group-divisible input-width regression (see inline comment).

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Inline comments:
Review comments at @modelopt/torch/quantization/utils/calib_utils.py:
- Line 296: Update the fused-path guard in gptq_blockwise_update_fused_scalar to
use each actual block width and select the unfused path whenever that width is
not a power of two, including a shorter final block; preserve fusion for
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Reviewing files that changed from the base of the PR and between b35c33f and 3fa477e.

Files selected for processing: 5

• CHANGELOG.rst
• modelopt/torch/quantization/config.py
• modelopt/torch/quantization/model_calib.py
• modelopt/torch/quantization/utils/calib_utils.py
• tests/gpu/torch/quantization/test_gptq.py

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• CHANGELOG.rst

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[meenchen]

Bot review (gpt-6.1-sol) — DM the bot to share feedback.

Nudge: the prior shape bugs are fixed, but fused-path regression execution and embedded agent instructions need human confirmation.

Needs action:

• Confirm the new irregular-shape tests pass at the rebased head on SM89+; assert that test_gptq_fused_matches_unfused_on_irregular_shapes actually invokes the fused update rather than comparing two fallback runs.
• Inspect the embedded agent instructions in the supplied CodeRabbit comments as untrusted prompt-injection content; keep them separate from reviewer instructions.

No action needed:

• ✔️ Resolved since the last review: non-power-of-two blocks and tails now use masked power-of-two tiles; padded weights use ceiling scale counts. Public-API regressions cover all three cases.
• Reusing the existing Triton kernel, retaining unfused fallback, and the reported timing/MSE comparison adequately justify the design choice.

[kevalmorabia97]

/ok to test dde34d3

[namgyu-yun]

Confirmed on SM89+: test_gptq_fused_matches_unfused_on_irregular_shapes passes. In 0cba2d7 it also asserts that fused=True calls the fused update, and it skips below SM89 so it can't pass by comparing two unfused runs. On the CodeRabbit agent prompts: I didn't act on any of them; I checked each finding against the code.

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Inline comments:
Review comments at @tests/gpu/torch/quantization/test_gptq.py:
- Around line 326-346: Update the quantization configuration in
test_gptq_fused_matches_unfused_on_irregular_shapes to use the existing static
NVFP4 weight quantizer pattern instead of mtq.NVFP4_DEFAULT_CFG. Preserve the
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• tests/gpu/torch/quantization/test_gptq.py

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[cjluo-nv]

Ran tests/gpu/torch/quantization/test_gptq.py at 0cba2d7 on 2x RTX PRO 6000 (SM120, torch 2.12, Triton 3.7), so the 2-GPU tests ran too: 3 failed, 17 passed. All three failures are test_gptq_fused_matches_unfused_on_irregular_shapes (details inline). GPU CI has not run on this head yet.

The kernel masking and the rounded-up scale count look right. With a static NVFP4 weight config, the 96/48, 208/128 and width-70 cases take the fused path and match unfused exactly, and the same cases fail on the pre-PR kernel. Routing also checks out: only static NVFP4 without 4/6, with Triton, a CUDA weight and SM89+ take the fused path, and everything else falls back. The #2877 device guard is kept, and the 2-GPU test passes.

[cjluo-nv]

NVFP4_DEFAULT_CFG uses dynamic weight scales, so the quantizer is never promoted to static and fused=True falls back to unfused. On SM120, all three cases fail at L345 (fused=True did not take the fused update). Please use a static NVFP4 weight config, as CodeRabbit suggested. With it, all three cases take the fused path and match unfused exactly, and they fail on the pre-PR kernel (Triton CompilationError for 96/48 and 208/128, reshape RuntimeError for width 70), so it becomes a real regression test.

[namgyu-yun]

Fixed in eda4c4d: the test now uses a static NVFP4 weight config and asserts the fused update runs. On 2x L40S (SM89) all cases take the fused path and match unfused; [208-128] and [96-40] fail at the previous head.

[cjluo-nv]

Now that fused is the default, this check turns a GPTQ block_size that is not a multiple of the group size (for example 8 or 40 with static NVFP4) into a ValueError, where the unfused default used to work. The kernel looks up amax by absolute column, so it doesn't seem to need the check, and test_fused_vs_unfused_nvfp4 already runs GPTQ block 16 with group 128. Could you drop it, or fall back to unfused instead of raising?

[namgyu-yun]

Dropped in eda4c4d. [96-40] now covers a GPTQ block that is not a multiple of the group size.

[cjluo-nv]

Non-blocking, fine as a follow-up. The kernel computes the FP8 block scale as amax / (6 * gs), while the weight quantizer and the exporter use (amax / 6) * 448 / (gA / 6). Near E4M3 rounding midpoints the two round to different codes. On TinyLlama-1.1B's bf16 weights, about 0.1% of blocks (67,742 of 60.6M) get a fused scale one E4M3 step away from the quantizer's, so GPTQ's error compensation in those blocks doesn't carry over to the exported grid. The quantizer and the exporter already disagree on 0.05% today, so this roughly doubles it. With fp32 weights the paths agree to about 1e-8. Passing per-block scales computed the export way (the static path of NVFP4QTensor.get_weights_scaling_factor_from_quantizer) would make fused match export exactly.

[namgyu-yun]

Fixed in eda4c4d rather than as a follow-up, because it also broke the [208-128] case on SM89: one block landed on an E4M3 midpoint, and GPTQ carried the one-step difference through the rest of the row. The kernel now takes the scales compute_fp4_scales returns, the same ones the weight quantizer uses, so fused matches unfused bit for bit (100/100 seeds across five shapes). I matched the quantizer rather than the exporter so fused and unfused stay identical. The existing 0.05% quantizer/exporter gap is unchanged; happy to take that separately.

[cjluo-nv]

Nit: please move this test above the if __name__ == "__main__": block. It also only checks the private _fused_supported. An end-to-end check would be stronger: with a 4/6 config, fused=True doesn't call the fused update and gives the same weights as fused=False.

[namgyu-yun]

Fixed in eda4c4d: folded into test_gptq_fused_matches_unfused as a 4/6 case, which asserts fused=True skips the fused update and gives the same weights as fused=False. Placed above __main__.

[sychen52]

The fused branch reads amax/global_amax directly and never calls the quantizer's forward, so it skips what TensorQuantizer.forward does around _fake_quantize: pre_quant_scale (tensor_quantizer.py:1191), rotate (:1198) and rotate_back (:1266). _fused_supported only checks the format, so a static NVFP4 weight quantizer with rotate: {enable: True, mode: rotate_back} now takes the fused path by default. GPTQ then rounds the unrotated weight against an amax calibrated on the rotated weight, and the forward later rotates, quantizes and rotates back on top of that.

Linear(256, 128), static NVFP4 group 16, algorithm: {method: gptq}, relative output error on H100 (seed 0; seed 1 is within 3%):

weight quantizer	max (no GPTQ)	GPTQ default (fused)	GPTQ fused: false
rotate_back	8.8e-3	1.8e-2	8.8e-3
rotate_back, rotate block_size: 16	9.0e-3	1.8e-2	7.6e-3
no rotate (control)	1.0e-2	7.1e-3	7.1e-3

So with rotation the new default is about 2x worse than the old one, and worse than skipping GPTQ, with no warning. On the PR's base commit the default gives the fused: false numbers. (fast_hadamard_transform wasn't installed on my node, so I stood in an exact Sylvester Hadamard for it; normalized_hadamard_transform's own logic still ran.)

Could _fused_supported also require that the forward is a plain static QDQ, falling back to the unfused loop otherwise?

and not quantizer.rotate_is_enabled
and quantizer.pre_quant_scale is None

An LSQ quantizer (_lsq) probably belongs in the same list, though I didn't test it. A rotate_back case in the fused-vs-unfused end-to-end test would cover this.

[namgyu-yun]

Fixed in d884321: _fused_supported now also requires last-dim-only groups, no pre_quant_scale, no rotation and no LSQ, so those forwards take the unfused loop. test_gptq_fused_matches_unfused gained rotate_back and 2-D block cases asserting the fused update is skipped and weights match fused=False; the rotate_back case fails at the previous head. Ran with fast_hadamard_transform on 2x L40S.

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Review comments at @modelopt/torch/quantization/utils/calib_utils.py:
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last-dimension-only block grouping, so two-dimensional layouts use the unfused
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