[OMNIML-6062] [11/21] Dequantize block-FP8 checkpoints as they load expert-parallel by shengliangxu · Pull Request #2743 · NVIDIA/Model-Optimizer

shengliangxu · 2026-10-12T00:56:58Z

What does this PR do?

Type of change: New feature

A native block-FP8 checkpoint, such as DeepSeek-V3, is dequantized to bf16 while it loads expert-parallel, through transformers' FineGrainedFP8Config(dequantize=True). Quantizers attach to bf16 weights.

On transformers older than 5.10.1, the loader also patches that dequantization in three ways:

Partial last block. Stock dequantization infers the block size by dividing the weight shape by the scale grid's, so it raises on a weight whose last 128x128 block is partial, such as DeepSeek-V3's kv_a_proj_with_mqa: (576, 7168) with a (5, 56) grid. It also raises on an empty scale grid. The patch tiles each block's scale and crops to the weight, using the checkpoint's weight_block_size.
Output dtype. Stock dequantization does not take the destination dtype. The patch emits the model's dtype.
Routed experts. The FP8 quantizer prepends its dequantization to every weight converter, so the fused-experts converter no longer starts with a MergeModulelist. The loader then assigns no per-expert index, and the expert-parallel shard slices each expert's rows instead of whole experts: a DeepSeek-V3 gate_up_proj loads as (256, 32, 7168) and fails calibration. The patch fuses the dequantization into the merge, so the converter starts with a MergeModulelist again and runs as upstream intends.

The patch installs once and survives a re-imported module. It steps aside on transformers 5.10.1 and later: upstream added the destination dtype there, plus E8M0 scale decoding that the override lacks.

Usage

Automatic: load_and_prepare_ep reads the checkpoint's quantization_config and picks this path for quant_method: fp8.

Testing

New tests/unit/torch/utils/test_fp8_dequant_patch.py, on CPU:
- the patched dequant against a blockwise reference;
- block size from the scale grid or the DeepSeek default;
- the requested output dtype;
- an empty scale grid passing the weight through;
- idempotent installs that keep the latest dtype;
- no double-wrapping of a re-imported module;
- a clean undo;
- transformers 5.10.1 keeping its own dequant;
- the fused expert converter, run on a real fused-experts converter: it starts with a MergeModulelist, and per-expert block-FP8 weights come out as the blockwise reference. It fails without the fusion, and if the fused op skips the dequantization or the merge. Skipped on transformers 5.5, which has no update_weight_conversions.
Mutation-checked: each test fails on at least one deliberate break of the code.
The block-size cases join the blockwise-reference table, and dtype and version cases that took the same branch as a kept one are dropped.
Locally on this revision, the reordered series rebased on main 264fd26: the full unit suite on transformers 5.18, and on 5.5 apart from the tests whose ONNX, TensorRT or diffusers packages that environment lacks; CI's transformers-free unit lane; pre-commit over the whole PR against main; and the Sphinx docs build. The same at every PR of the stack.
The GPU results here were measured on earlier revisions of this branch; this revision's GPU tests run in CI only.
On GPUs: DeepSeek-V3 (block FP8) loads, calibrates and exports expert-parallel through this path (see [21/21]).

Before your PR is "Ready for review"

Is this change backward compatible?: ✅ Additive; runs only on the expert-parallel load of a block-FP8 checkpoint.
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?: N/A. The feature's entry lands with [21/21].
Did you get Claude approval on this PR?: ❌ Not yet.

Additional Information

Part 11 of 21 of expert-parallel PTQ for Hugging Face MoE models; merges after [10/21].

Summary by CodeRabbit

New Features
- Added support for loading models from native block-FP8 checkpoints, converting weights to the model’s configured dtype during loading.
- Improved handling of partial or empty scale grids, varied tensor shapes, and per-expert weights.
- Added compatibility with older Transformers versions while preserving built-in behavior in newer versions.

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📝 Walkthrough

The loader now configures dequantization for native FP8 checkpoints. For Transformers versions before 5.10.1, a compatibility patch handles scale grids and expert merging. Unit tests cover dequantization cases, patch behavior, and version gating.

Changes: FP8 checkpoint loading

Layer / File(s)	Summary
FP8 dequantization and validation `modelopt/torch/utils/plugins/model_load_utils.py`, `tests/unit/torch/utils/test_fp8_dequant_patch.py`	Adds dequantization support for partial and empty scale grids on Transformers versions before 5.10.1. Tests compare varied block shapes and output dtypes with reference results, and verify that newer versions bypass the patch.
Expert conversion and patch lifecycle `modelopt/torch/utils/plugins/model_load_utils.py`, `tests/unit/torch/utils/test_fp8_dequant_patch.py`	Combines FP8 dequantization with expert merging to preserve per-expert indexing. Tests cover converter ordering, repeated patching, hook wrapping, and restoration.
FP8 loader configuration `modelopt/torch/utils/plugins/model_load_utils.py`	Configures FP8 checkpoint loading with `FineGrainedFP8Config(dequantize=True)`, applies the patch with the model dtype, and logs the selection. The MXFP4 configuration remains the alternative branch.

Priority: ➖ Normal

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant load_and_prepare_ep
  participant from_pretrained
  participant CompatibilityPatch
  load_and_prepare_ep->>from_pretrained: load FP8 checkpoint with FineGrainedFP8Config(dequantize=True)
  load_and_prepare_ep->>CompatibilityPatch: apply patch with model dtype

Merge Risk: 🟡 Moderate · up to 78b38

Loading block-FP8 checkpoints with older Transformers versions can silently produce incorrectly scaled weights in edge cases. Those weights would then be used for calibration and export. Before merging, make the patch reject scale grids that do not match their weights, or document why the current fallbacks are valid.

Pre-merge checks: 5 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 69.57% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 23 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 5 passed

Check name	Status	Explanation
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		No listed security anti-pattern was introduced. The PR changes only `modelopt/torch/utils/plugins/model_load_utils.py` and a unit test; no modelopt/example dependency manifests changed. The added code…
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.
Title check		The title clearly identifies the expert-parallel loading change for block-FP8 checkpoints and matches the main purpose of the pull request.

Full details: Security Anti-Patterns

Explanation

No listed security anti-pattern was introduced. The PR changes only modelopt/torch/utils/plugins/model_load_utils.py and a unit test; no modelopt/example dependency manifests changed. The added code contains no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, external-input eval()/exec(), or # nosec comment. Existing trust_remote_code handling remains caller-configurable with a default of False.

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codecov · 2026-10-12T01:13:07Z

Codecov Report

❌ Patch coverage is 84.50704% with 11 lines in your changes missing coverage. Please review.
✅ Project coverage is 61.14%. Comparing base (a6931e1) to head (9e57bc9).

Files with missing lines	Patch %	Lines
modelopt/torch/utils/plugins/model_load_utils.py	84.50%	11 Missing ⚠️

Additional details and impacted files

@@                            Coverage Diff                            @@
##           shengliangx/hfep-6b-ep-dense-sharding    #2743      +/-   ##
=========================================================================
+ Coverage                                  61.12%   61.14%   +0.02%     
=========================================================================
  Files                                        648      648              
  Lines                                      71991    72061      +70     
=========================================================================
+ Hits                                       44003    44062      +59     
- Misses                                     27988    27999      +11

Flag	Coverage Δ
unit	`61.14% <84.50%> (+0.02%)`	⬆️

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github-actions · 2026-10-12T02:43:10Z

PR Preview Action v1.8.1
🚀 View preview at https://NVIDIA.github.io/Model-Optimizer/pr-preview/pr-2743/
Built to branch `gh-pages` at 2026-10-20 21:48 UTC. Preview will be ready when the GitHub Pages deployment is complete.

shengliangxu · 2026-10-12T03:16:29Z

/ok to test ed1ebbf

shengliangxu · 2026-10-12T10:28:50Z

/ok to test 6db2eb0

shengliangxu · 2026-10-13T01:48:22Z

/ok to test 14f1610

shengliangxu · 2026-10-13T21:02:12Z

/ok to test f647855

shengliangxu · 2026-10-16T17:15:25Z

/ok to test 99afebc

shengliangxu · 2026-10-16T19:23:57Z

/ok to test 19cb5c0

shengliangxu · 2026-10-16T22:22:39Z

/ok to test 8293770

shengliangxu · 2026-10-16T22:43:32Z

/ok to test d5ccfc3

shengliangxu · 2026-10-16T23:36:02Z

/ok to test 78b3825

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Inline comments:
Review comments at @modelopt/torch/utils/plugins/model_load_utils.py:
- Around line 1480-1482: Update the empty-scale-grid handling in the block-FP8
dequantization path so it returns the converted weight only when q is also
empty; raise an error when q contains elements but scales is empty or the grid
dimensions are zero.
- Around line 1487-1494: Update the _blk helper to derive the candidate block
size as ceil(dim / sgrid), then accept it only when ceil(dim / candidate block
size) equals sgrid; raise a clear ValueError for grids that cannot be matched
instead of falling back to cfg_blk. Preserve the existing configured-block
ceil-grid case.

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modelopt/torch/utils/plugins/model_load_utils.py

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shengliangxu · 2026-10-16T23:55:48Z

/ok to test 341d9b8

shengliangxu · 2026-10-16T23:57:30Z

@coderabbitai review

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shengliangxu · 2026-10-17T00:58:42Z

/ok to test e17117f

shengliangxu · 2026-10-20T00:59:28Z

/ok to test 9f4bb69

shengliangxu · 2026-10-20T01:13:32Z

/ok to test 1cd98c4

shengliangxu · 2026-10-20T02:01:20Z

/ok to test e1a6aa2

A native block-FP8 checkpoint (e.g. DeepSeek-V3) is dequantized to bf16 while it loads, through transformers' FineGrainedFP8Config, since quantizers attach to bf16 weights. Its dequant is corrected on transformers < 5.10.1 for weights whose last 128x128 block is partial, and made to emit the model's dtype. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…out the session Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The patch and its module flags sit in model_load_utils with the expert-parallel loader that applies them, so the test drives and resets them there. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The patch fuses the Fp8Dequantize that transformers prepends to the routed-expert converter into its MergeModulelist, so the loader still gives each expert its tensor_idx. Only the install was tested. The new test runs the patched update_weight_conversions on an expert converter and the resulting chain on per-expert block-FP8 weights. It fails if the converter no longer leads with a MergeModulelist, or if the fused op skips the dequant or the merge. transformers 5.18's chain passes an output_dtype the patched dequant does not take, so the chain runs that version's own dequant; the patched arithmetic has its own tests. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

- The two block-size cases, a grid finer than the configured block and no block configured, become rows of the blockwise-reference table, which gains configured and grid columns. - The dtype test keeps its unrequested (bf16) and requested (fp16) cases; requesting bf16 or fp32 took the same branch. - The version test keeps the 5.10.1 boundary; 5.17.0 took the same branch. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The pre-5.10.1 dequant patch emitted a non-empty weight unscaled when its scale grid was empty. That only happens when a weight and its scales were sliced apart, as transformers 5.9's expert tensor_idx bug did on expert-parallel loads, so it loaded wrong weights without a word; upstream's own dequant fails on such a grid. It also fell back to the configured block for a grid neither that block nor an even split gives, scaling rows wrongly. Raise in both cases; an empty weight still passes through its empty grid. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

shengliangxu · 2026-10-20T21:10:20Z

/ok to test 9e57bc9

shengliangxu added this pull request to stack #2755 October 12, 2026 00:57

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from f354b3b to ed1ebbf Compare October 12, 2026 02:35

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from ed1ebbf to 6db2eb0 Compare October 12, 2026 08:39

shengliangxu changed the title ~~[8/19] Dequantize block-FP8 checkpoints as they load expert-parallel~~ [OMNIML-6062] [8/19] Dequantize block-FP8 checkpoints as they load expert-parallel Oct 12, 2026

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 6db2eb0 to 6bcb1ca Compare October 13, 2026 00:53

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 6bcb1ca to 14f1610 Compare October 13, 2026 01:32

shengliangxu removed this pull request from stack #2755 October 13, 2026 17:42

shengliangxu added this pull request to stack #2775 October 13, 2026 17:43

shengliangxu changed the title ~~[OMNIML-6062] [8/19] Dequantize block-FP8 checkpoints as they load expert-parallel~~ [OMNIML-6062] [8/20] Dequantize block-FP8 checkpoints as they load expert-parallel Oct 13, 2026

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 14f1610 to f647855 Compare October 13, 2026 19:38

shengliangxu removed this pull request from stack #2775 October 13, 2026 19:38

shengliangxu added this pull request to stack #2781 October 13, 2026 19:40

shengliangxu changed the title ~~[OMNIML-6062] [8/20] Dequantize block-FP8 checkpoints as they load expert-parallel~~ [OMNIML-6062] [9/21] Dequantize block-FP8 checkpoints as they load expert-parallel Oct 13, 2026

shengliangxu removed this pull request from stack #2781 October 14, 2026 00:13

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from f647855 to a40c380 Compare October 14, 2026 00:14

shengliangxu added this pull request to stack #2786 October 14, 2026 00:14

shengliangxu changed the title ~~[OMNIML-6062] [9/21] Dequantize block-FP8 checkpoints as they load expert-parallel~~ [OMNIML-6062] [10/22] Dequantize block-FP8 checkpoints as they load expert-parallel Oct 14, 2026

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from a40c380 to d407931 Compare October 14, 2026 01:47

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 48fabad to 99afebc Compare October 16, 2026 17:13

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 99afebc to 19cb5c0 Compare October 16, 2026 19:21

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 19cb5c0 to 8293770 Compare October 16, 2026 22:22

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from 8293770 to d5ccfc3 Compare October 16, 2026 22:43

shengliangxu force-pushed the shengliangx/hfep-6c-fp8-dequant branch from d5ccfc3 to 78b3825 Compare October 16, 2026 23:35

coderabbitai Bot reviewed Oct 16, 2026

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Comment thread modelopt/torch/utils/plugins/model_load_utils.py Outdated

Comment thread modelopt/torch/utils/plugins/model_load_utils.py Outdated

shengliangxu added 6 commits October 20, 2026 21:06

Log the block-fp8 dequant from rank 0 by the live process group, with…

0c2b3a8

…out the session Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Test the block-FP8 dequant patch where it lives, with the loader

b5ef225

The patch and its module flags sit in model_load_utils with the expert-parallel loader that applies them, so the test drives and resets them there. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

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