[shengliangxu]

Note

The expert-parallel PTQ series is now a tree instead of one stack (re-cut 2026-10-27, from main 1521053), so PRs that do not depend on each other can be reviewed and merged in parallel. This PR was [5/21]. Rebased onto main 7c3c868 later the same day, after #2739 merged, and onto main f03651e that night.

Base: main, now that #2736 has merged (2026-10-28). The diff below is this PR's alone.

Re-cut: this PR's diff is unchanged; only its base moved. Review fixes since are listed at the end of Testing.

Series layout

• main
  • [OMNIML-6073] [2/3] Quantize fused experts under grouped_mm; leave transposed layouts unquantized #2745 fused experts under grouped_mm (merged)
    • [OMNIML-6073] [2/2] Calibrate fused experts on grouped_mm and torch_a2a instead of forcing eager #3074 calibration on grouped_mm
      • [OMNIML-6073] Quantize Nemotron-H's experts under grouped_mm, with their wrapper in its model folder #3067 Nemotron-H's experts under grouped_mm
      • [OMNIML-6071] Calibrate a distributed model on per-rank data #2740 calibration on per-rank data
  • [OMNIML-6064] Load each rank's checkpoint shard straight into a meta-built model #2739 meta-DCP checkpoint load (merged)
  • [OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736 distribution plan, run group, collectives (merged)
    • [OMNIML-6081] [2/2] Fold fused experts' quantizers into their weights shard-locally #2750 keep-fused fold
    • [OMNIML-6082] [3/3] Write a sharded model's HF checkpoint shards from each tensor's owner rank #2751 distributed-writer pieces
      • [OMNIML-6083] Write a sharded model's HF checkpoint without gathering it #2752 no-gather export (on [OMNIML-6082] [3/3] Write a sharded model's HF checkpoint shards from each tensor's owner rank #2751, [OMNIML-6081] [2/2] Fold fused experts' quantizers into their weights shard-locally #2750, [OMNIML-6054] [2/2] Route load_pretrained by the distribution plan #2779 and [OMNIML-6071] Calibrate a distributed model on per-rank data #2740)
        • [OMNIML-6084] Export an expert-parallel model through the no-gather writer #2753 expert-parallel export
    • [OMNIML-6054] Decide how a model is distributed, from the model built on the meta device #2737 plan decision (this PR)
      • [OMNIML-6054] [2/2] Route load_pretrained by the distribution plan #2779 load routing
        • [OMNIML-6060] [3/3] Refuse an expert-parallel load that would calibrate the experts wrong #2741 expert-parallel loader (on [OMNIML-6054] [2/2] Route load_pretrained by the distribution plan #2779, [OMNIML-6073] Quantize Nemotron-H's experts under grouped_mm, with their wrapper in its model folder #3067 and [OMNIML-6071] Calibrate a distributed model on per-rank data #2740)
          • [OMNIML-6061] Load the dense part sharded, and size the load warmup to each rank #2742 dense sharding
            • [OMNIML-6062] Dequantize block-FP8 checkpoints as they load expert-parallel #2743 block-FP8 dequantization
              • [OMNIML-6060] Load an expert-parallel plan through load_pretrained #2780 expert-parallel load routing
                • [OMNIML-6076] [3/3] Apply the model spec when deciding and loading expert-parallel #2746 expert parallelism in the model specs
                  • [OMNIML-6063] [3/3] Load a checkpoint its model spec decompresses through the expert-parallel loader #2747 on-the-fly decompression
                  • [OMNIML-6078] [2/2] Add the Kimi-K2.5 expert-parallel adapter #2748 Kimi-K2.5 and Kimi-VL adapters
                  • [OMNIML-6079] [2/2] Add the Kimi-K3 expert-parallel adapter #2749 Kimi-K3 adapter
                  • [OMNIML-6085] [3/3] Run expert-parallel PTQ from hf_ptq.py #2754 expert-parallel PTQ from hf_ptq.py (on [OMNIML-6076] [3/3] Apply the model spec when deciding and loading expert-parallel #2746 and [OMNIML-6084] Export an expert-parallel model through the no-gather writer #2753)

What does this PR do?

Type of change: New feature

resolve_distribution_plan(ckpt_path) decides how a run spreads a model over its ranks. It returns a DistributionPlan (#2736) for every model, MoE or dense, and for a single process too. It is the one place a run decides this: #2779 loads the model by the plan and records the plan on it, and every later step reads it from there.

• From the model, not estimates. Rank 0 builds the model on the meta device, using the class that loads it whole: a causal LM, or for a VLM the class that carries its LM head. A trusted remote VLM whose auto_map registers AutoModelForImageTextToText builds through that class, with its LM head. It decides from the model's parameters and then discards it, and leaves the auto class it built through as it was: building a remote-code model through an auto class registers its class there, as it is, and rank 0's later load through that class would then build it without the GenerationMixin every other rank's first load adds. Rank 0's Kimi-K3 lacked generate while the other ranks' had it, and an expert-parallel preview deadlocked.
• EP degree (ep_size, or MODELOPT_EP_DEGREE).
  • auto takes the largest degree that divides both the world and the routed-expert count.
  • The routed experts are the ones the config's base_model_ep_plan names. They are found by the rule the expert-parallel loader ([OMNIML-6060] [3/3] Refuse an expert-parallel load that would calibrate the experts wrong #2741) uses, so the decision and the load cannot disagree.
  • An explicit degree must divide the routed-expert count.
• Dense layout (dense, or MODELOPT_FSDP_SIZE, which takes auto, 1 to replicate, or world or the world size to shard).
  • auto FSDP2-shards the dense part when a whole copy of it plus the rank's share of the routed experts would take more than 60% of one GPU's memory.
  • Sharding over part of the world is refused.
• Every rank reads the checkpoint first. Each rank makes the checkpoint local and reads its config inside the failure-carrying all_reduce ([OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736), before the plan's broadcast. A rank that cannot see the checkpoint fails every rank with its own error, instead of leaving rank 0 waiting in the broadcast.
• The checkpoint the load reads. resolve_distribution_plan takes revision, token, cache_dir, local_files_only and subfolder as load_pretrained does, so the plan and its config are for the checkpoint the load reads.
• Rank 0 decides, and every rank gets its plan (broadcast_from, [OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736). The inputs are read locally: the knobs, this GPU's memory, the files on this node. Ranks deciding for themselves could disagree, and ranks loading different plans hang. If rank 0 fails, every rank raises.
• A single process decides without building anything, and reads no knob: a shell still exporting a multi-GPU run's MODELOPT_EP_DEGREE or MODELOPT_FSDP_SIZE must not reshape it. It holds the whole model, unless the caller's own dense="fsdp" asks for FSDP2 on one rank.

Measuring the meta model avoids the gaps of estimating from config fields, which this PR's earlier revision did: it ignored GQA and tied embeddings, assumed bf16, and treated a config missing the fields it read as one that fits a GPU.

Why not hf_ptq's get_model, or Accelerate's sizing. The decision builds through the library's own meta build, build_meta_causal_lm, which main's FSDP2 load uses too. get_model lives in the hf_ptq example, which the library cannot import, and is a single-process device_map loader: it requires architectures and takes its class from the first entry, beside the example's own speculative, pack-quantized and MXFP4 branches. Accelerate's infer_auto_device_map and compute_module_sizes say which device holds each module of such a load. The plan needs one number instead: what one rank holds when expert parallelism splits the routed experts 1/ep_size and keeps the rest whole, a single sum over the meta model's parameters (re-tied first, as init_empty_weights unties them).

The class that builds a VLM with its head takes the checkpoint's remote class only under trust_remote_code, a MODELOPT_EP_DEGREE that is not a number is refused by name, and a single process parses its own ep_size as a distributed run does, refusing a bad one the same way.

Usage

# torchrun --nproc_per_node 8 ...
from modelopt.torch.utils.plugins.model_load_utils import resolve_distribution_plan

plan, hf_config = resolve_distribution_plan("Qwen/Qwen3-30B-A3B")
# DistributionPlan(world_size=8, ep_size=8, dense='replicate')

Testing

• Tests in tests/unit/torch/utils/test_model_load_utils.py (the decision) and tests/unit/torch/utils/test_moe_a2a.py (the MoE helpers), the files of the modules they cover. They run on CPU, build tiny real configs on the meta device, and answer rank and size as torchrun would. They cover:
  • the knobs, read at call time; expert counting; EP-degree selection;
  • routed experts found under any model prefix, named by their style or as their grouped-GEMM parameters' parent;
  • the class that builds a whole model, a VLM's with its LM head;
  • the decision:
    • a single process, whatever knobs its shell exports; an EP-capable MoE; an MoE without an EP plan;
    • the dense fit from exact parameter bytes, counting what expert parallelism leaves whole and the rank's share of the routed experts, so an expert-dominated MoE shards its dense part;
    • knob and argument overrides;
    • refused partial sharding and indivisible EP degrees;
    • a VLM decided from its whole model;
    • a model that cannot be built on meta, named in the error;
    • the meta build leaving the auto class's registrations as it found them (fails without the fix);
  • on two real gloo ranks (cpu_dist_workers): both take rank 0's plan, though rank 1 exports a different dense layout, and rank 1 decides nothing; a rank that cannot see the checkpoint fails both ranks with its error, before the broadcast. Rank 0's own failures are broadcast_from's, tested in [OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736.
• On 2 GPUs with transformers 5.18, a tiny Kimi-K3 at EP 2 with previews on: before this fix rank 0's model alone had no generate, so rank 0 moved on to calibration while rank 1 decoded in lockstep, and the run deadlocked (TORCH_DISTRIBUTED_DEBUG=DETAIL: an all-reduce on one rank against an all-to-all on the other); with it, both ranks preview, calibrate and export.
• Mutation-checked: 30 mutants, each failing a test; and, since, a rank deciding for itself, the plan sent from the wrong rank, a single process reading the knobs, the checkpoint read outside the all_reduce, the experts' share left out of the dense fit, the Hub options not passed on, a single process's ep_size not parsed, and a trusted remote VLM's registered image-text class not taken.
• Near-duplicate tests are merged into one test per behavior, and the env-knob, ep * dp == world and no-EP-plan tests, which the decision tests cover, are dropped. Each drop is checked by mutation.
• The auto-class table has an untrusted remote VLM and a trusted remote image-text VLM, and a non-numeric MODELOPT_EP_DEGREE names the knob, as a bad ep_size in a single process names the argument.
• The Hub options locate the checkpoint against an offline Hugging Face cache with two revisions and a subfolder.
• Locally on this revision, the stack rebased on main c6fa613: pre-commit over the whole PR against main, at every PR of the stack; and at the stack's tip the full tests/unit on transformers 5.18 and 5.5 and on torch 2.8, and the hf_ptq example tests, each with the same failures as main in that environment. CI runs the full unit suite.
• Re-cut on 2026-10-27 (see the note at the top): pre-commit, mypy included, passes at this head over the whole change against main, and the series' PRs merged together give a tree byte-identical to the former stack's tip. This PR's own unit tests pass at this head on CPU (torch 2.14, transformers 5.18); CI runs the full unit suite.
• Review fixes, 2026-10-27: an encoder-decoder model builds through its seq2seq class; MODELOPT_EP_DEGREE reads auto in any case; a hybrid plan names its routed experts by both styles; the expert count is read from the config holding the plan; a docstring note on a rank 0 without a GPU. Each behaviour fix has a test that fails without it; pre-commit, mypy included, and this PR's own unit tests pass at this head.
• Rebased on 2026-10-27 onto main 7c3c868, after [OMNIML-6064] Load each rank's checkpoint shard straight into a meta-built model #2739 merged: pre-commit, mypy included, and this PR's own unit tests pass at this head, and the series' PRs merged together add no unit-test failure over main (tests/unit/torch utils, export, quantization and models, on transformers 5.18 and 5.5.4 and on torch 2.8).
• Review suggestions, 2026-10-27 evening: the refusal of an EP degree that cannot shard the experts names the knob (MODELOPT_EP_DEGREE) or the argument the degree came from.
• Review fixes, 2026-10-27 night: the meta build leaves AutoModel's and AutoModelForCausalLM's registrations as it found them; a routed-expert config whose expert-parallel plan names no module of the model is refused unless the degree is 1; an explicit degree that does not divide the world names the knob or argument it came from. Each new test fails without its change.
• Review suggestions, 2026-10-27, last round: the routed-expert match computes each module name's wildcard form (every layer number replaced) once instead of once per key, with the same matches; build_meta_causal_lm's docstring states the registrations its restore assumes, and resolve_distribution_plan's that a caller passes the local checkpoint directory.
• Review fix, 2026-10-27, last round: a VLM-shaped config that AutoModelForCausalLM recognizes (Phi-4-multimodal, the only such config in transformers 5.5.4 or 5.18) builds through it, head included, rather than through AutoModel. The new table row fails without the change.
• Review suggestion, 2026-10-27, last round: resolve_distribution_plan's docstring says each rank returns its own copy of the config, rank 0's deciding the plan.
• Review suggestion, 2026-10-28: _choose_ep_degree's docstring says why the automatic EP degree is the largest: it holds the fewest experts per rank, leaving the most memory for activations, and every degree above 1 runs the same two all-to-alls per MoE layer.
• Declined review findings, 2026-10-27:
  • The plan decision builds the model on meta even when ep_size=1 and dense are both given. Rank 0 takes 0.54-0.65 s and 8-208 MB of host memory on 232B-1T configs, the FSDP2 load builds the same meta model anyway, and a model that cannot be built on meta loads with distribution=, which skips the decision.
  • _auto_class falls back to AutoModel for a remote VLM whose AutoModelForCausalLM class is named *ForCausalLM. None of the remote checkpoints the series supports is one: Kimi-VL, Kimi-K2.5, Kimi-K3 and Step-3.7-Flash register *ForConditionalGeneration, and DeepSeek-VL2 registers no auto class. Qwen3-Omni, which AutoModel cannot build, is not supported on main.
• Review suggestion, 2026-10-28, second round: the plan decision's MoE helpers and the three auto classes are imported at module top, nothing having deferred them.
• Review suggestion, 2026-10-28, fifth round: modelopt.torch.utils.distributed is imported at module top as dist; [OMNIML-6054] [2/2] Route load_pretrained by the distribution plan #2779 drops its four function-local imports of it.
• Rebased on 2026-10-28, late night, onto [OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736 at cf4597dcbeb, on main 1ca8bd1fc4d (after [OMNIML-6073] [1/3] Gather only FSDP2's own parameters in its weight-access window #3088 and [OMNIML-6073] [2/3] Quantize fused experts under grouped_mm; leave transposed layouts unquantized #2745 merged). The one conflict was with main's [OMNIML-5987] [3/3] Support Qwen text-only FSDP2 AutoQuant loading and export #2676: build_meta_causal_lm keeps this PR's auto_class parameter, now defaulting to None, so a call without one still gets main's text-only-VLM factory. The plan decision passes _auto_class(...) as before, and the registry restore covers whichever class builds. This PR's tests and test_model_load_utils.py pass on transformers 5.18 (90) and 5.5.4, and pre-commit passes against main.
• Rebased on 2026-10-28, late night, onto main bdc45368d10 after [OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736 merged; [OMNIML-6052] Add the distribution plan, a run-scoped process group, and collectives that fail together #2736's squash has the tree of its last head, so this PR's tree is unchanged.
• Review fixes, 2026-10-29: the plan decision re-ties the meta model before sizing it, so a tied embedding and LM head count once (init_empty_weights unties them); the dense-fit test is sized from a normally built model, tied and untied, on either side of the 60% share. An encoder-decoder config that names no architecture (a bare T5Config or WhisperConfig) builds through its seq2seq class, checked through the public resolver too. Each fails its test without the change (mutation-checked). The routed-expert match's local transformers import names its reason (the optional [hf] extra). Pre-commit passes against main; this PR's tests, test_model_load_utils.py and test_moe_a2a.py pass on CPU with transformers 5.18 and 5.5.4.
• Review fix, 2026-10-29: the tests moved into the files of the modules they cover (CONTRIBUTING.md): the plan decision's into test_model_load_utils.py, moe.py's helpers' into test_moe_a2a.py (a test_moe.py in tests/unit already belongs to modelopt/torch/models/moe.py, and pytest refuses two test modules of one basename in a run), and the plain-module MoE they share into tests/_test_utils/torch/moe_models.py; test_resolve_distribution_plan.py is gone. Test bodies are unchanged apart from the helpers' names. Pre-commit passes against main; the moved tests pass on CPU with transformers 5.18 and 5.5.4.
• Review suggestions, 2026-10-29, Claude's approving round: build_meta_causal_lm's dead local import of AutoModelForImageTextToText is gone; routed experts the built model has but the config does not count (under num_experts, num_local_experts, n_routed_experts or num_routed_experts) are refused unless the degree is 1, as a plan naming no module of the model is, instead of being replicated on every rank silently. The new test fails without the refusal (mutation-checked). Pre-commit passes against main; this PR's tests pass on CPU with transformers 5.18 and 5.5.4.

Before your PR is "Ready for review"

• Is this change backward compatible?: ✅ Additive.
• 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 [OMNIML-6085] [3/3] Run expert-parallel PTQ from hf_ptq.py #2754.
• Did you get Claude approval on this PR?: ✅ Approved 2026-10-29 at 1f9b0ee7e98; its two suggestions are taken since.

Additional Information

Part 5 of 21 of expert-parallel PTQ for Hugging Face MoE models; merges after #2736.

Summary by CodeRabbit

• New Features
  • Added automatic model distribution planning, including expert- and data-parallel degree selection and dense-parameter sharding based on GPU memory.
  • Added support for environment-configured parallelism settings and model selection and sizing for vision-language models.
  • Added validation for model configurations and parallelism choices.
• Bug Fixes
  • Distribution plans are now consistent across ranks, and planning failures are propagated to all ranks.

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Walkthrough

The change adds model and routed-expert discovery, selects EP and dense-layout settings, and resolves a distribution plan across ranks. Unit tests cover discovery, planning decisions, model-class selection, broadcasts, and checkpoint visibility errors.

Changes: Model distribution planning

Layer / File(s)	Summary
Model and routed-expert discovery modelopt/torch/utils/moe.py, modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_resolve_distribution_plan.py	Adds expert-count and EP-degree helpers, matches routed-expert keys to model modules, and selects the Transformers model class for meta-model construction. Tests cover config discovery, routed-expert matching, and model-class selection.
EP and dense-layout decisions modelopt/torch/utils/env.py, modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_resolve_distribution_plan.py	Adds environment accessors and decision logic for EP degree and dense replication or FSDP sharding. Tests cover defaults, overrides, divisibility, memory thresholds, and invalid settings.
Plan resolution and rank coordination modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_resolve_distribution_plan.py	Adds checkpoint config loading and rank-0 plan computation and broadcast. Tests cover broadcast behavior and checkpoint-visibility errors.

Priority: Normal

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Caller
  participant resolve_distribution_plan
  participant Rank0
  participant MetaModel
  participant OtherRanks
  Caller->>resolve_distribution_plan: checkpoint path and planning options
  resolve_distribution_plan->>Rank0: config and planning request
  Rank0->>MetaModel: build meta model and discover routed experts
  Rank0->>resolve_distribution_plan: distribution plan or error
  resolve_distribution_plan->>OtherRanks: broadcast plan
  OtherRanks->>resolve_distribution_plan: adopt plan

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Merge Risk: Moderate · up to 7c4fe

For some composite or remote vision-language checkpoints, the distribution plan can pick a wrong expert-parallel degree or a wrong dense layout. Fix both selection paths before merging.

Pre-merge checks: 5

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Title check		The title clearly describes the main change: determining model distribution from a model built on the meta device.
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Explanation

PASS. The PR changes only three modelopt Python modules and one test file. The changed code has no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), eval/exec on external input, or # nosec. Transformers calls pass the caller-controlled trust_remote_code value, and the public resolver defaults it to False; no hardcoded trust_remote_code=True was added. No pyproject.toml or requirements.txt dependency changes are present.

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[codecov]

Codecov Report

❌ Patch coverage is 99.27536% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 80.44%. Comparing base (2b900c8) to head (2a6290b).

Files with missing lines	Patch %	Lines
modelopt/torch/utils/plugins/model_load_utils.py	98.97%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2737      +/-   ##
==========================================
+ Coverage   78.09%   80.44%   +2.35%     
==========================================
  Files         662      651      -11     
  Lines       76114    73117    -2997     
==========================================
- Hits        59439    58821     -618     
+ Misses      16675    14296    -2379     

Flag	Coverage Δ
examples	?
examples-diffusers	19.88% <0.00%> (-0.04%)	⬇️
examples-gpt-oss	13.46% <0.00%> (-0.02%)	⬇️
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examples-megatron_bridge	26.47% <0.00%> (-0.29%)	⬇️
examples-specdec_bench	13.24% <0.00%> (-0.02%)	⬇️
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PR Preview Action v1.8.1
Preview removed because the pull request was closed.
2026-10-29 19:06 UTC

[shengliangxu]

/ok to test 8b9651f

[shengliangxu]

/ok to test fe5b268

[shengliangxu]

/ok to test 74bbbf9

[shengliangxu]

/ok to test 9074ecf

[shengliangxu]

/ok to test 2a6290b

[kevalmorabia97]

The plan decision and the collectives are correct, and the tests cover the logic that matters (107 passed). Non-blocking: build the meta model from copy.deepcopy(hf_config), since the meta build sets _attn_implementation_internal and _experts_implementation_internal on rank 0 only, and resolve_distribution_plan returns that same config.