[OMNIML-6072] [2/21] Make amax synchronization correct under data-parallel calibration by shengliangxu · Pull Request #2744 · NVIDIA/Model-Optimizer

shengliangxu · 2026-10-12T00:57:00Z

What does this PR do?

Type of change: Bug fix (for data-parallel calibration, which the series introduces)

With data-parallel calibration a module can be exercised on some ranks and not others: a sparsely routed MoE expert, or anything a given rank's batch never reaches. Its _amax then exists on only part of the group. Every rank must still join the collective, or the participant set mismatches and NCCL hangs. So the whole question is what a rank with no amax contributes.

Per-tensor amax. The shape is known locally, so a rank without one contributes a scalar zero, the identity for MAX over non-negative amax.
- The same all-reduce carries a presence flag and the calibrator's dtype.
- If nobody calibrated the quantizer, it stays without an amax, so the usual amax is None handling still applies.
- A calibrated amax of zero, from an all-zero weight, survives as on main.
Static per-channel or per-block amax. A rank without one cannot know its shape, so ranks first agree, through one MAX all-reduce of a presence flag that every rank joins. If nobody calibrated it, it is skipped. If everybody did, the real reduce runs. A genuine split raises on every rank at once; raising only where the amax is missing would leave the other ranks blocked in a collective that never completes.
Dynamic-block quantizers (NVFP4 activations) store only a scalar global scale, so they are treated as per-tensor rather than pushed into the static path's hard error.
Dtype and device.
- Every rank contributes float32, on the device of the group's backend: NCCL hangs rather than raising on a dtype mismatch. This was seen on Kimi-K3, NVFP4 EP64, with 8 ranks holding an amax and 56 not.
- Each rank then keeps its calibrator's dtype, so later bf16 writes (MSE calibration) round exactly as on main. A rank that never calibrated the quantizer adopts the group's dtype.

max_calibrate keeps main's sync structure (data-parallel always, expert-parallel filtered by _should_sync_amax_across_ep), so Megatron's numerics are unchanged. Three changes:

A rank joins the data-parallel sync even without an amax.
The tensor-parallel sync, of linears and of the KV-cache quantizers, likewise runs on every rank whether or not it calibrated the quantizer. It is gated on the quantizer being enabled, which the config makes the same on every rank, instead of on a local amax; on main a calibrated rank waited for a peer that skipped it.
HF fused experts (_share_input_amax_across_ep) share their input amax over the EP group before the completeness check, so experts that received no tokens take the layer's value instead of failing it. Megatron's experts keep main's check-then-sync, including its "MoE calibration incomplete" error.

A failed amax all-reduce now names its likely cause: NCCL running out of memory for its buffers.

Usage

N/A: internal to calibration.

Testing

tests/unit/torch/quantization/test_dist.py, under gloo on CPU, two ranks, so every PR runs the multi-rank cases:
- a rank that never calibrated still joins the sync;
- a calibrated zero amax survives;
- every rank ends in the calibrator's dtype, and calibrated ranks take the group max, for an INT8 and an NVFP4 dynamic-block quantizer (the latter synced as per-tensor);
- a quantizer no rank calibrated stays uncalibrated, per-tensor and per-channel;
- uneven per-channel calibration fails on every rank, on a group with a 10 s timeout, so a rank left waiting fails rather than hangs;
- a rank without an amax still joins the tensor-parallel sync, for a column-parallel linear and for the KV-cache quantizers;
- only modules that opt in share expert input amax before the completeness check, while Megatron's raise as on main.
Each fails on the code before its fix, or under a mutation of the sync it checks.
tests/gpu/torch/quantization/test_amax_sync_distributed.py keeps only what needs NCCL: a rank without an amax contributes its zero on the collective's CUDA device, when one rank calibrated and when none did (2 GPUs; the other multi-rank cases moved to the unit test above, since the PR runner has 1 GPU).
The GPU results here were measured on earlier revisions of this branch, including the earlier, larger GPU file (7 passed on a 4-GPU node); this revision's GPU tests run in CI only.
Against main, inside a one-rank process group, as Megatron, FSDP2 and DDP runs calibrate: FP8 max, FP8 MSE and INT8 MSE calibration leave every amax value and dtype identical to main's (48 of 48). An FP8 export with an all-zero weight succeeds, as on main.
A calibrated zero amax and the uncalibrated quantizers are now one parametrized test, and the never-calibrated-rank test, a case the dtype test runs, is dropped. Each drop is checked by mutation.
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 series' model runs (see [21/21]) calibrate sparsely routed MoEs at 4 to 64 ranks through this path.

Before your PR is "Ready for review"

Is this change backward compatible?: ✅ Single-process calibration and Megatron's synchronization are unchanged.
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. Covered by the feature's entry in [21/21].
Did you get Claude approval on this PR?: ❌ Not yet.

Additional Information

Part 2 of 21 of expert-parallel PTQ for Hugging Face MoE models; merges after [1/21].

Summary by CodeRabbit

Bug Fixes
- Improved distributed quantization calibration so ranks can synchronize values even when some ranks have no local calibration data.
- Calibration remains unset when no ranks have data, preserves calibrated zero values and data types, and flags inconsistent per-channel calibration across ranks.
- Expert-parallel calibration shares input values before MoE completeness checks where configured.
- Tensor-parallel and KV-cache synchronization now handles partially calibrated ranks correctly.
- Distributed synchronization failures now raise a contextual error instead of being reduced to a warning.

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📝 Walkthrough

Distributed amax synchronization now includes ranks with missing local calibration values. Opted-in expert modules synchronize enabled input quantizers across expert-parallel groups before MoE completeness validation. Tests cover dtype, zero, missing, uneven, tensor-parallel, KV-cache, and expert-input cases.

Changes: Distributed Amax Calibration

Layer / File(s)	Summary
Coordinate amax across ranks `modelopt/torch/quantization/nn/modules/tensor_quantizer.py`, `tests/gpu/torch/quantization/test_amax_sync_distributed.py`, `tests/unit/torch/quantization/test_dist.py`	`TensorQuantizer.sync_amax_across_distributed_group` coordinates calibration presence, amax values, and dtype metadata across ranks. Static non-per-tensor quantizers raise on uneven calibration. Per-tensor and dynamic-block quantizers propagate calibrated values to uncalibrated ranks.
Synchronize opted-in expert inputs `modelopt/torch/quantization/model_calib.py`, `modelopt/torch/quantization/plugins/huggingface.py`, `tests/unit/torch/quantization/test_dist.py`	`max_calibrate` synchronizes enabled input quantizers for opted-in `QuantModule`s before MoE completeness validation. `_QuantFusedExperts` opts in. Tensor-parallel and KV-cache synchronization also includes enabled quantizers without local amax values.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Bug fix

Sequence Diagram(s)

sequenceDiagram
  participant max_calibrate
  participant QuantModule
  participant TensorQuantizer
  participant EPGroup
  max_calibrate->>QuantModule: Check _share_input_amax_across_ep
  QuantModule-->>max_calibrate: Return sharing flag
  max_calibrate->>TensorQuantizer: Synchronize enabled input quantizers
  TensorQuantizer->>EPGroup: Reduce calibration state and amax
  EPGroup-->>TensorQuantizer: Return reduced state and amax
  max_calibrate->>max_calibrate: Check MoE calibration completeness

Suggested reviewers: kevalmorabia97

Merge Risk: 🟡 Moderate · up to 13838

Distributed calibration now keeps ranks without local statistics in the synchronization step. However, for per-channel quantizers configured through block sizes, tensor-parallel ranks can disagree about whether to join the synchronization, which can hang the run. Make tensor-parallel participation depend on configuration rather than on state that changes during forward before merging. The earlier test-import concern is fixed.

Pre-merge checks: 5 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 51.85% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 27 functions across 5 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 5 passed

Check name	Status	Explanation
Title check		The title clearly and concisely describes the main change: correcting amax synchronization for data-parallel calibration.
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 reviewed range changes three ModelOpt Python modules and no examples or dependency files. Added code only implements distributed amax synchronization and an expert-sharing flag. The added di…
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.

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Explanation

PASS. The reviewed range changes three ModelOpt Python modules and no examples or dependency files. Added code only implements distributed amax synchronization and an expert-sharing flag. The added diff contains no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, external-input eval()/exec(), or # nosec. SECURITY.md states these rules apply to non-test code, and the dependency-file diff is empty.

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github-actions · 2026-10-12T02:41:03Z

PR Preview Action v1.8.1
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shengliangxu · 2026-10-12T02:55:15Z

/ok to test 5f115f2

codecov · 2026-10-12T02:55:25Z

Codecov Report

❌ Patch coverage is 94.73684% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 79.01%. Comparing base (12c9ee8) to head (669b19c).

Files with missing lines	Patch %	Lines
.../torch/quantization/nn/modules/tensor_quantizer.py	93.18%	3 Missing ⚠️

Additional details and impacted files

@@                         Coverage Diff                          @@
##           shengliangx/hfep-3-a2a-transport    #2744      +/-   ##
====================================================================
+ Coverage                             79.00%   79.01%   +0.01%     
====================================================================
  Files                                   660      660              
  Lines                                 74589    74638      +49     
====================================================================
+ Hits                                  58926    58975      +49     
  Misses                                15663    15663

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shengliangxu · 2026-10-12T08:58:21Z

/ok to test d6a4b30

shengliangxu · 2026-10-13T04:41:55Z

/ok to test 235ecbd

shengliangxu · 2026-10-13T21:18:30Z

/ok to test 9572dc5

shengliangxu · 2026-10-16T23:35:46Z

/ok to test 2eecf56

shengliangxu · 2026-10-20T00:59:13Z

/ok to test 13838ea

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Inline comments:
Review comments at @modelopt/torch/quantization/model_calib.py:
- Around line 493-501: Update the TP synchronization condition around
quantizer.sync_amax_across_distributed_group so participation uses a
rank-invariant configuration-time predicate, not the forward-mutated
quantizer.axis or quantizer.block_sizes values. Ensure all ranks with the same
supported static per-channel configuration enter the collective consistently,
while preserving the existing enabled/disabled behavior.

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modelopt/torch/quantization/model_calib.py
modelopt/torch/quantization/nn/modules/tensor_quantizer.py
tests/unit/torch/quantization/test_dist.py

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shengliangxu · 2026-10-20T01:13:16Z

/ok to test b688a42

shengliangxu · 2026-10-20T02:01:02Z

/ok to test e846690

shengliangxu · 2026-10-20T21:10:03Z

/ok to test 0e39626

shengliangxu · 2026-10-21T17:44:33Z

/ok to test 22e0b93

shengliangxu · 2026-10-21T18:33:06Z

/ok to test 19f832e

shengliangxu · 2026-10-22T00:53:54Z

/ok to test a08f3b8

shengliangxu · 2026-10-22T06:47:21Z

/ok to test 74cf59b

shengliangxu · 2026-10-22T18:45:32Z

/ok to test 5ae6586

shengliangxu · 2026-10-22T19:10:26Z

/ok to test 4f9e94b

With data-parallel calibration a module can be exercised on some ranks and not others -- a sparsely routed MoE expert, or anything a given rank's batch never reaches -- so _amax ends up present on only a subset of the group. Every rank must still join the collective or the participant set mismatches and NCCL hangs, which makes "what does a rank with no amax contribute" the whole problem. For a per-tensor amax the answer is easy: the shape is known locally, so a have-not contributes a scalar zero, the identity for MAX over non-negative amax. If the all-reduced result is zero then nobody calibrated it, and the buffer is dropped so downstream `amax is None` handling still applies -- keeping it as 0 would quantize the module to nothing. A static per-channel or per-block amax has no locally-known shape, so a have-not cannot fabricate a correctly-shaped zero. Ranks agree first, via one MAX all-reduce of a packed presence flag that every rank joins: nobody calibrated it is fine and skipped; everybody did falls through to the real reduce; a genuine split raises on every rank at once. Raising only where the amax is missing would leave the other ranks blocked in a collective that never completes. A dynamic-block quantizer -- NVFP4 activations -- stores only a scalar global scale; its block_sizes drive runtime scales and do not describe the stored amax. Classifying by "has block_sizes" would push it down the static path and turn the ordinary uneven-calibration case into a hard error, so it is treated as per-tensor. Every rank also contributes the same dtype and device. A calibrated amax keeps the calibrator's dtype while a have-not's zero is float32, and NCCL hangs on a mismatch rather than raising (seen on Kimi-K3, NVFP4 EP64: 8 ranks with an amax, 56 without, and the collective never returned), so the amax is cast to float32 first. The device comes from the group's backend, which every rank shares -- CUDA under NCCL, CPU otherwise -- so a gloo group on a CPU host works. max_calibrate keeps main's sync structure -- DP always, EP filtered by _should_sync_amax_across_ep -- so Megatron's numerics are unchanged, with three changes: a rank joins the DP sync even without an amax (disabled quantizers are skipped, which is symmetric since it is a config property); the input amax of expert-parallel experts is shared over the EP group BEFORE the completeness check, so experts that received no tokens pick up the layer's value instead of failing it; and the allocator's cached pool is returned first, since NCCL allocates its buffers outside it. A failed amax all-reduce now says it is most likely that out-of-memory rather than surfacing as a bare CUDA error. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Two reads go through an Optional that is set by then, which the mypy run in pre-commit (and so in CI) rejects: the amax every rank holds once it reaches the MAX all-reduce (calibrated, or the zero synthesised just above), and the parallel state _has_expert_parallelism has already checked. Assert both. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The sync decided that no rank had calibrated a quantizer when the reduced amax was zero, and dropped it. A calibrated amax can legitimately be zero -- an all-zero weight -- and dropping it made the FP8 export fail on the missing _amax for any run with a process group (Megatron, FSDP2, DDP). The ranks now agree through a presence flag carried in the same all-reduce. The sync also promoted every amax to float32 for the collective and left it there, so later bf16 writes (MSE calibration) stopped rounding as before. Each rank now keeps its calibrator's dtype, and a rank that never calibrated the quantizer adopts the group's. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…he release Sharing the routed experts' input amax over the expert-parallel group before the completeness check exists for HF fused experts, whose experts see only the tokens routed to them. It also applied to Megatron's experts, which then stopped failing on incomplete calibration and silently took the group max. Only modules that opt in (_share_input_amax_across_ep, set by the HF fused experts wrapper) share early; Megatron keeps main's check-then-sync. max_calibrate also ran gc.collect() and emptied the CUDA cache on every call, which AWQ and SmoothQuant make once per linear: ~45 s on an 8B model. The distributed forward loop already returns the cache after its last forward, right before the sync this was for. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

With one rank nobody lacks the amax, so nothing raises and pytest.raises fails inside the worker. That failure is a BaseException the worker pool does not catch: the worker dies without reporting, and every later test on the session's pool waits on it. CI's PR GPU runner has one GPU. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Each of them spawned two ranks, about 14 s of the linux unit job apiece. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The amax sync is a collective every rank of the group joins, and it handles a rank with no amax of its own. The tensor-parallel callers still skipped such a rank, for linears and for the KV-cache quantizers alike, so a rank that calibrated the quantizer waited in the sync for a peer that never joined it. They now gate on whether the quantizer is enabled, as the data-parallel and expert-parallel sync does: the config enables the same quantizers on every rank, and a disabled one, which nothing quantizes with, joins no collective. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The GPU test file sized its world from the GPU count, so on the 1-GPU PR runner each "some ranks missing" case ran on one rank that held the amax, and its one two-rank case needed two GPUs: no PR exercised the multi-rank behavior. The sync logic runs on gloo through cpu_dist_workers instead: - the uneven per-channel raise, which every rank must reach together, on a group that gives up in seconds rather than hang; - a per-channel quantizer no rank calibrated, which is not an error; - an NVFP4 dynamic-block quantizer, which syncs as a per-tensor one; - the group's max over ranks that all calibrated, which no unit test checked: a rank keeping its own amax went unnoticed. The GPU file keeps the check only NCCL can make, where a rank without an amax puts the zero it contributes, and asks for two GPUs: with one, no rank lacks the amax. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

- test_amax_sync_with_a_rank_that_never_calibrated is the one-calibrated half of test_the_sync_leaves_every_rank_the_calibrators_dtype, which runs the same case in bf16 and checks the dtype too. - test_a_calibrated_zero_amax_survives_the_sync and test_a_quantizer_no_rank_calibrated_stays_uncalibrated become one test_the_sync_keeps_a_calibrated_zero_apart_from_uncalibrated over calibrated-zero, uncalibrated and uncalibrated-per-channel, each case keeping its assert. A have-not rank that keeps no amax or takes zero, a zero amax read as uncalibrated, nobody's amax becoming zero, and a per-channel quantizer nobody calibrated raising each fail a remaining test. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

shengliangxu · 2026-10-22T20:59:09Z

/ok to test 669b19c

Fridah-nv · 2026-10-22T22:17:15Z

Design suggestion (non-blocking): one sync entry point for calibration

#2736 gives the control plane labeled, fail-together collectives. Calibration doesn't get that yet: max_calibrate walks the modules and issues the DP, EP and TP syncs itself, and TensorQuantizer.sync_amax_across_distributed_group uses raw dist.all_reduce. The hangs in the deck (amax dtype mismatch, TP sync gated on the amax) came from this layer, and a hang there doesn't show whether the algorithm or the distribution is at fault.

Suggestion (EP only for now; TP/DP can move behind it later):

Add one entry point, e.g. sync_calibration_stats(model), that reads the recorded plan and _parallel_state and does whatever sync the layout needs. Algorithms call it and never touch a process group. It is a no-op without a plan.
Label its collectives per stage (e.g. per decoder layer) and carry a failure flag in the presence-flag all-reduce you already pack. A failing rank then raises everywhere, naming the stage.
Raise when an algorithm that isn't synced under EP (GPTQ, MSE, local-Hessian?) runs with an EP plan, instead of running unsynced.

Which algorithms are meant to work under EP in this stack?

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

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from 06a5f2d to 5f115f2 Compare October 12, 2026 02:35

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from 5f115f2 to d6a4b30 Compare October 12, 2026 08:39

shengliangxu changed the title ~~[9/19] Make amax synchronization correct under data-parallel calibration~~ [OMNIML-6072] [9/19] Make amax synchronization correct under data-parallel calibration Oct 12, 2026

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from d6a4b30 to e28bd09 Compare October 13, 2026 00:53

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from e28bd09 to 235ecbd 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-6072] [9/19] Make amax synchronization correct under data-parallel calibration~~ [OMNIML-6072] [9/20] Make amax synchronization correct under data-parallel calibration Oct 13, 2026

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from 235ecbd to 9572dc5 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-6072] [9/20] Make amax synchronization correct under data-parallel calibration~~ [OMNIML-6072] [10/21] Make amax synchronization correct under data-parallel calibration Oct 13, 2026

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

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from 9572dc5 to 6e7ca4f Compare October 14, 2026 00:14

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

shengliangxu changed the title ~~[OMNIML-6072] [10/21] Make amax synchronization correct under data-parallel calibration~~ [OMNIML-6072] [11/22] Make amax synchronization correct under data-parallel calibration Oct 14, 2026

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from d69c5f1 to 2eecf56 Compare October 16, 2026 23:35

shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from 2eecf56 to 13838ea Compare October 20, 2026 00:58

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shengliangxu force-pushed the shengliangx/hfep-7-amax-sync branch from 13838ea to b688a42 Compare October 20, 2026 01:12

shengliangxu mentioned this pull request Oct 22, 2026

[OMNIML-6059] [1/21] Add the DP=EP all-to-all expert transport #2738

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shengliangxu added 10 commits October 22, 2026 20:56

Run the amax-sync tests on the shared gloo workers

5b72623

Each of them spawned two ranks, about 14 s of the linux unit job apiece. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Date test_amax_sync_distributed 2026, as LICENSE_HEADER does

669b19c

Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

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