[OMNIML-6071] [8/21] Calibrate a distributed model on per-rank data by shengliangxu · Pull Request #2740 · NVIDIA/Model-Optimizer

shengliangxu · 2026-10-12T00:56:50Z

What does this PR do?

Type of change: New feature

The pieces a calibration loop needs once the model is spread across ranks.

shard_dataloader gives each rank its own shard of the data.
- Map-style datasets use a DistributedSampler with drop_last off, so every rank runs the same number of steps; a rank that exits early hangs the others on the per-step collectives.
- An IterableDataset gets a deterministic strided copy of the stream, split across the loader's worker processes so that each sample is still seen once. It is not main's vlm_dataset_utils._ShardedIterable, which pads each rank to a count its loader knows: this wraps any loader's stream, which may have no length, and splits it across that loader's workers.
distributed_forward_loop builds the forward_loop that mtq.quantize calls. It moves each batch to the device it is given and runs the full distributed forward, so the expert-parallel collectives fire inside it, with KV caching off as on the single-process path.
- A model whose distribution plan ([4/21]) is expert-parallel passes the ep_mesh its experts are sharded over, and the loop re-plants the torch_a2a forward wrappers that quantizer insertion clobbers on transformers >= 5.16. Any other model passes none. A mesh missing for an expert-parallel model, or given for another, raises.
- It returns the allocator's reserved pool before the first forward and after the last. NCCL's buffers live outside the torch allocator, so a full reserved pool starves them. A process that never started CUDA skips this, and the garbage collection it costs.
Every rank runs the same batch size. Equal step counts need equal batch sizes, and hf_ptq's default --batch_size 0 sizes calibration with get_max_batch_size on each rank: it measured every visible GPU, and on an out-of-memory error halved the batch and probed again, a different number of collective-bearing forwards per rank. For a model whose plan spreads it over several ranks it is now collective: each rank measures its own GPU and probes once, and every rank takes the smallest size any rank allows, reduced with all_reduce(..., min) ([4/21]). A single process is unchanged.

Usage

from modelopt.torch.utils.distributed import distributed_forward_loop, rank, shard_dataloader, size

loader = shard_dataloader(calib_loader, rank(), size())
# ep_mesh: the mesh an expert-parallel model's experts are sharded over; None for any other model
forward_loop = distributed_forward_loop(model, loader, device=device, ep_mesh=ep_mesh)
model = mtq.quantize(model, quant_cfg, forward_loop=forward_loop)

Testing

New tests/unit/torch/utils/test_distributed_calibration.py (21 cases), on CPU, in one process. It covers:
- strided shards that cover the data exactly once, with equal batch counts on uneven splits, for map-style and streaming datasets, including the split across DataLoader workers, run in-process with the worker info faked rather than by forking workers;
- every loader setting carried over;
- the wrapper re-planting, on the planned modules only and without stacking;
- the loop running this rank's shard through the top-level model, with use_cache off and the allocator cache freed around it in a process that started CUDA, and not in one that did not;
- the ep_mesh refused when missing for an expert-parallel plan or given for another.
The worker-split test fails on the code before that fix.
tests/unit/torch/utils/test_dataset_utils.py, on two real gloo ranks (cpu_dist_workers) with only the CUDA memory queries faked per rank: each rank measures only its own GPU, probes once, and both take the smaller batch size. A rank keeping its own size, measuring every GPU, or skipping the reduction fails it, the last after the group's 10 s timeout rather than by hanging; main's single-process retry test still passes.
Mutation-checked: deciding the reduction or the mesh other than by the plan fails a test (5 mutants).
The shard tests are one table over dataset kind and world size, the two replant tests are one, and cases a remaining test covers are 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.

Before your PR is "Ready for review"

Is this change backward compatible?: ✅ Additive; get_max_batch_size changes only for a model distributed over several ranks.
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 8 of 21 of expert-parallel PTQ for Hugging Face MoE models; merges after [7/21].

Summary by CodeRabbit

New Features
- Distributed calibration now shards map-style and streaming datasets across ranks, keeping batch counts aligned and preserving data loader settings.
- Calibration supports expert- and tensor-parallel models with improved device handling, cache management, and compatibility with parallel model wrappers.
- Distributed batch-size detection probes each rank’s local GPU and selects the smallest supported batch size across ranks, helping prevent capacity mismatches.

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📝 Walkthrough

The changes add per-rank batch-size probing, distributed calibration data-loader sharding, and a calibration forward loop. The loop validates expert-parallel mesh configuration, restores tensor-parallel wrappers, manages caching and CUDA memory, and processes batches.

Changes: Distributed Calibration

Layer / File(s)	Summary
Per-rank batch-size probing `modelopt/torch/utils/dataset_utils.py`, `tests/unit/torch/utils/test_dataset_utils.py`	Each rank probes its local GPU and contributes its regulated batch size to a minimum reduction. Device-specific probes do not retry smaller batches.
Calibration data sharding `modelopt/torch/utils/distributed.py`, `tests/unit/torch/utils/test_distributed_calibration.py`	Adds map-style and iterable dataset sharding. The rebuilt loader preserves its settings and handles uneven or short inputs as tested.
Calibration forward loop and wrapper restoration `modelopt/torch/utils/distributed.py`, `tests/unit/torch/utils/test_distributed_calibration.py`	Adds expert-parallel mesh validation, A2A wrapper restoration, temporary cache disabling, CUDA allocator cleanup, optional device movement, and calibration forwards.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~40 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Caller
  participant distributed_forward_loop
  participant shard_dataloader
  participant Model
  Caller->>distributed_forward_loop: Create distributed calibration callback
  distributed_forward_loop->>shard_dataloader: Iterate this rank's calibration shard
  distributed_forward_loop->>Model: Restore A2A wrappers and run each batch
  Model-->>distributed_forward_loop: Complete calibration forward

Suggested reviewers: kevalmorabia97

Merge Risk: 🟡 Moderate · up to 3ac4c

On multi-GPU runs, distributed calibration can choose a batch size that was never tested on the GPU and may run out of memory, with no fallback to smaller batches. Expert-parallel models can also get their all-to-all forward wrapped twice, which can break the first calibration forward. Single-process calibration is unchanged. Fix both issues before merging.

Pre-merge checks: 5 · 1

Failed checks: 1 warning

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Docstring Coverage		Docstring coverage is 51.72% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 58 functions across 4 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 summarizes the primary change: calibrating distributed models using per-rank data.
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Security Anti-Patterns		PASS. The pull request changes only `modelopt/torch/utils/dataset_utils.py` and `modelopt/torch/utils/distributed.py`; it adds no example or dependency changes. Added lines contain no unsafe `torch.lo…
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PASS. The pull request changes only modelopt/torch/utils/dataset_utils.py and modelopt/torch/utils/distributed.py; it adds no example or dependency changes. Added lines contain no unsafe torch.load, allow_pickle=True, hardcoded trust_remote_code=True, external-input eval/exec, or # nosec comments.

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github-actions · 2026-10-12T02:31:16Z

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Codecov Report

❌ Patch coverage is 98.76543% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 61.44%. Comparing base (493d053) to head (ecd1975).

Files with missing lines	Patch %	Lines
modelopt/torch/utils/distributed.py	98.52%	1 Missing ⚠️

Additional details and impacted files

@@                         Coverage Diff                          @@
##           shengliangx/hfep-4-meta-dcp-load    #2740      +/-   ##
====================================================================
+ Coverage                             61.40%   61.44%   +0.03%     
====================================================================
  Files                                   648      648              
  Lines                                 71952    72028      +76     
====================================================================
+ Hits                                  44185    44260      +75     
- Misses                                27767    27768       +1

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shengliangxu · 2026-10-12T03:24:22Z

/ok to test 963e78a

shengliangxu · 2026-10-12T10:39:32Z

/ok to test 935bd7f

shengliangxu · 2026-10-13T01:55:58Z

/ok to test 96159d7

shengliangxu · 2026-10-13T19:58:36Z

/ok to test 540716e

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Review comments at @modelopt/torch/utils/distributed.py:
- Around line 674-684: Replace the one-time _modelopt_a2a_forward_reinstalled
guard in _reinstall_a2a_forwards with per-instance forward snapshots. Capture
each module’s forward when distributed_forward_loop is built, reinstall only
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modelopt/torch/utils/distributed.py
tests/unit/torch/utils/test_distributed_calibration.py

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shengliangxu · 2026-10-20T01:13:26Z

/ok to test f8c8606

shengliangxu · 2026-10-20T02:01:13Z

/ok to test c10b54a

shengliangxu · 2026-10-20T21:10:15Z

/ok to test 2c5c8e7

shengliangxu · 2026-10-21T17:44:16Z

/ok to test 03dd0c9

shengliangxu · 2026-10-21T18:32:46Z

/ok to test 34fc41b

shengliangxu · 2026-10-22T00:53:39Z

/ok to test 496386a

shengliangxu · 2026-10-22T01:14:57Z

/ok to test 0b3a046

shengliangxu · 2026-10-22T06:47:06Z

/ok to test 56e7760

shengliangxu · 2026-10-22T16:55:55Z

/ok to test 2a03601

shengliangxu · 2026-10-22T18:45:16Z

/ok to test d2899bd

shengliangxu · 2026-10-22T19:10:41Z

/ok to test bcb25e4

shengliangxu · 2026-10-22T20:24:24Z

/ok to test 3cb3f4d

The pieces a calibration loop needs once the model is spread across ranks: - shard_dataloader gives each rank its own data shard: a DistributedSampler for map-style datasets, with drop_last off so every rank runs the same number of steps (a rank that exits early hangs the others on the per-step collectives), and a deterministic strided copy of the stream for an IterableDataset. - distributed_forward_loop builds the forward_loop mtq.quantize calls: it moves each batch to this rank's device and runs the full distributed forward, so the expert-parallel collectives fire inside it, with KV caching off as on the single-process path. It re-plants the torch_a2a forward wrappers that quantizer insertion clobbers on transformers >= 5.16 (on the session's EP mesh), and returns the allocator's reserved pool both before the first forward and after the last -- NCCL's communication buffers live outside the torch allocator, so a full reserved pool starves them. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Cover how every rank calibrates on its own data and keeps the all-to-all in place: - shard_dataloader: strided shards that together cover the data exactly once, equal batch counts on an uneven split, and every loader setting carried over, for map-style and streaming datasets; - _reinstall_a2a_forwards: re-planting the expert-parallel wrappers that quantizer insertion drops, on the planned modules only and without stacking; - distributed_forward_loop: this rank's shard through the top-level model, use_cache off while it runs, the allocator cache freed around it. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Each worker process iterates its own copy of the stream, and every one of them yielded the rank's whole shard, so with num_workers=2 each sample was calibrated on twice. Have each worker take every num_workers-th group: the shards still cover the data once, with equal counts on every rank. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

hf_ptq's default --batch_size 0 sizes calibration with get_max_batch_size, which each rank ran on its own: it measured every visible GPU, probed with a forward, and on an out-of-memory error halved the batch and probed again. Under expert parallelism those forwards hold all-to-alls, so a rank that retried ran more of them than the rest, and ranks could settle on different batch sizes. Different sizes mean different numbers of calibration steps, and a rank that finishes early leaves the others waiting in a collective. Inside a distributed session get_max_batch_size is now collective: each rank measures its own GPU only and probes with a single forward, and every rank takes the smallest size any rank allows, through the new min-reduced session.calib_batch_size. Outside a session it is unchanged. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Every rank takes the value get_max_batch_size returns, and nothing reads the batch size back from the session, so the ranks agree on it (ModelOptSession.agree, combined with min) rather than store it as a synced attribute. Every rank still gets the smallest size any rank's GPU allows. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

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

…h, reduce over the live group distributed_forward_loop takes the device to move batches to and the expert-parallel mesh whose all-to-all forwards it re-installs, where it took the session that carried both. The calibration batch size is still the smallest any rank allows, reduced with all_reduce over the live process group, each rank measuring its own GPU. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…ibution plan get_max_batch_size agrees on one batch size across ranks when the model's recorded plan spreads it over several, rather than whenever a multi-rank process group is live. distributed_forward_loop takes the ep_mesh exactly when the plan is expert-parallel: an expert-parallel model without its mesh would run its experts without an EP group at the first step, and a mesh for any other model would be ignored. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

… whole stream The rank shard splits each stream's groups between the DataLoader workers, so a dataset that already splits itself by get_worker_info() is split twice and drops samples. hf_ptq loads with num_workers=0; say so for other callers. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

… CUDA distributed_forward_loop collects garbage and empties the CUDA cache before the first calibration forward and after the last, so NCCL can allocate its buffers outside the caching allocator. A process that never started CUDA has no pool to free, yet each call still paid a full garbage collection, about a second on a large heap: nearly 12 s of the unit run across this file's forward-loop tests. The release now runs only once CUDA is initialized. The forward-loop test that walked a 4-case shard grid keeps one case; shard_dataloader's own tests cover the striding. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The test started two DataLoader workers per rank, forked from pytest's multithreaded process, which warns that fork may deadlock; in a sandbox where the workers could not use shared memory it hung until its timeout. The split it guards is decided in the shard's __iter__ from get_worker_info(), so the test now runs each worker's pass in this process as the worker it is told it is. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

vlm_dataset_utils._ShardedIterable already strides an iterable dataset across ranks, but it keeps ranks in step by padding each to a per-rank count the VLM loader knows. shard_dataloader wraps any loader's stream, which may have no length, so it drops the trailing partial group instead, and it must also split across the loader's workers, which the VLM loader never starts. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The test simulated rank 1 of two by stubbing all_gather_object and the world size, and asserted the tuple this rank put on the wire. It now runs get_max_batch_size on both ranks of the shared gloo workers, each with its own GPU's memory faked, and checks that both take the smaller size through the real reduction, measuring only their own GPU with one probe forward. A rank left out of the reduction gives up on a group with a 10 s timeout instead of hanging the run. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Merge the shard tests that differ only in dataset kind or world size: - the strided map-style test, the iterable strided-and-replay test and the world-size-one test become one table over {map-style, iterable} x world size {1, 2, 3}, which also checks the replay for both kinds; - the map-style padding test and the iterable trailing-drop test become one uneven-split test that keeps both kinds' asserts. Fold the _reinstall_a2a_forwards replant test into the forward-loop replant test (its router, colwise and children asserts; the rank-0 log line it also checked is wording). Drop, each checked by breaking the code it guards: - the no-stacking test: the forward-loop replant test runs two passes and fails when a repeat call wraps again; - the no-tp-plan no-op case: removing that early return changes nothing, so the case caught nothing (the no-mesh case stays: it is the only guard of its early return); - the device test's no-device case: the shard-routing test passes no device and reads the values back. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The strided iterable shard drops a trailing incomplete group so every rank yields the same count. A stream with fewer samples than ranks has no complete group at all, so every rank silently calibrated on nothing. Refuse it instead, as the VLM loader's own sharding already does for num_samples < dp_size. Every rank sees the same stream, so all of them refuse it at the first batch, before any forward. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

shard_dataloader forwards the original loader's non-sampler settings to the rebuilt per-rank DataLoader, but not drop_last, so a loader that drops its incomplete final batch got one again. Forward it on both paths. Every rank holds the same number of samples (the sampler pads, the stream keeps whole groups), so ranks still run the same number of batches. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

shengliangxu · 2026-10-22T20:59:22Z

/ok to test ecd1975

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

shengliangxu force-pushed the shengliangx/hfep-5-distributed-calibration branch from 7f91f94 to 963e78a Compare October 12, 2026 02:24

shengliangxu force-pushed the shengliangx/hfep-5-distributed-calibration branch from 963e78a to 935bd7f Compare October 12, 2026 08:39

shengliangxu changed the title ~~[5/19] Calibrate a distributed model on per-rank data~~ [OMNIML-6071] [5/19] Calibrate a distributed model on per-rank data Oct 12, 2026

shengliangxu force-pushed the shengliangx/hfep-5-distributed-calibration branch from 935bd7f to c3ccb86 Compare October 13, 2026 00:53

shengliangxu force-pushed the shengliangx/hfep-5-distributed-calibration branch from c3ccb86 to 96159d7 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-6071] [5/19] Calibrate a distributed model on per-rank data~~ [OMNIML-6071] [5/20] Calibrate a distributed model on per-rank data Oct 13, 2026

shengliangxu force-pushed the shengliangx/hfep-5-distributed-calibration branch from 96159d7 to 540716e 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-6071] [5/20] Calibrate a distributed model on per-rank data~~ [OMNIML-6071] [6/21] Calibrate a distributed model on per-rank data Oct 13, 2026

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

shengliangxu force-pushed the shengliangx/hfep-5-distributed-calibration branch from 540716e to 177c692 Compare October 14, 2026 00:14

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

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shengliangxu added 16 commits October 22, 2026 20:56

Reduce the calibration batch size through the renamed all_reduce

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

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