[OMNIML-6076] [13/21] Describe expert parallelism in the model specs by shengliangxu · Pull Request #2746 · NVIDIA/Model-Optimizer

shengliangxu · 2026-10-12T00:57:05Z

What does this PR do?

Type of change: New feature

Before it can shard a model's experts, the expert-parallel path needs three facts about the model:

which modules are the routed experts, and how they shard;
which transformers release first ships that information;
for a VLM, which nested config the plan must be attached to.

All three are facts about the architecture, so they belong with the model's other facts in modelopt.torch.models.

EPSpec, a new ModelSpec section, and apply_distributed_support, the one applier that reads it. Most models become data in the spec package they already had:
- deepseek_v3 carries the plan stock transformers does not ship;
- qwen3_moe and minimax_m3_vl carry only the transformers release their own plan needs.
- An existing plan on the config always wins, since overwriting it would substitute our idea of the layout for the model's.
One thing stays code: EPSpec.prepare, the escape hatch for checkpoints that ship their own modeling code ([18/21] to [20/21]). A spec whose plan only prepare attaches states that plan too (EPSpec.prepared_ep_plan), so it can be read without running prepare, which patches the model's code. Nemotron-H needs none. Its gate emits logits and the block runs its own top-k, so the experts receive global expert ids, which the all-to-all experts forward ([1/21]) routes on directly.
Nemotron-H-Omni is registered separately. Its experts live in the language sub-model, and transformers aggregates a sub-model's plan up to the root with its module prefix, so a plan on the VLM root would match nothing, silently. A test pins the placement and that the two spellings share one plan. transformers ships the Omni model itself from 5.18, which its spec records; expert parallelism still runs on the checkpoint's own code, whose llm_config the plan targets.
EPSpec.unsupported refuses a model whose own plan would route it into the expert-parallel path before that path can handle it. The refusal comes before anything is loaded and points to FSDP2.
- GPT-OSS and Llama-4 store their experts transposed, which the fused-experts export cannot write (GPT-OSS also interleaves gate and up).
- Llama-4's MoE block also routes tokens itself rather than through the experts interface.
The decision and the load apply the spec. [12/21] carried this before the series was reordered.
- Unless the run rules expert parallelism out (a single process, or an EP degree of 1), resolve_distribution_plan applies the model's spec to the config before rank 0 builds the model on meta, and returns the prepared config. Without it, DeepSeek-V3 would show no experts to shard.
- A load handed that config does not prepare it again, since a model's preparation can patch its modeling code; a load that decides for itself prepares it once.
- A run that rules expert parallelism out applies no spec, so a model whose spec refuses expert parallelism, like GPT-OSS, still loads FSDP2-sharded (--use_fsdp2, [21/21]).
- At an EP degree of 1, which skips the spec, the check that refuses an MoE held whole on every rank ([12/21]) still finds a spec-supplied plan's experts. It reads the plan the spec would supply, or the one it states prepare attaches, without applying anything. DeepSeek-V3 at MODELOPT_EP_DEGREE=1, replicated, is refused like a native-plan MoE instead of loading whole on every rank.
- That refusal points an MoE whose spec refuses expert parallelism (GPT-OSS, Llama-4) at FSDP2 alone (--use_fsdp2 in hf_ptq, or MODELOPT_FSDP_SIZE=world): raising its EP degree would only reach the spec's refusal.
- Rank 0's knobs decide whether the spec applies, and rank 0 sends that decision as it does the plan: a rank whose own environment skipped the spec would load without the expert-parallel plan the others shard by, and fail once the mesh is up.

Usage

from modelopt.torch.utils.moe import apply_distributed_support

hf_config = apply_distributed_support(hf_config.model_type, hf_config, ckpt_path=path)
# raises NotImplementedError for gpt_oss / llama4 / llama4_text, before any weight loads

Testing

tests/unit/torch/models/test_ep_specs.py:
- every registered plan is well formed, and every EP section declares something;
- the Omni placement and plan sharing;
- the applier's version floor, plan attachment, existing-plan precedence and prepare ordering;
- the refusals: GPT-OSS, Llama-4 and Llama-4-text are refused with their configs untouched, and a refusal comes before the rest of the section.
tests/unit/torch/utils/test_resolve_distribution_plan.py and test_load_pretrained_distribution.py gain the spec's cases, which [12/21] carried before the reorder:
- the spec applied before the decision, DeepSeek-V3's plan coming only from its spec, and the returned config carrying it;
- no spec for a run that rules expert parallelism out, by argument or by MODELOPT_EP_DEGREE=1, so GPT-OSS's spec does not refuse an FSDP2 run;
- on two real gloo ranks (cpu_dist_workers), every rank applying the spec, or not, as rank 0 decided, whatever its own knob says;
- DeepSeek-V3 refused when left whole on every rank, at an explicit EP degree of 1 too, as a native-plan MoE is, and allowed FSDP2 instead; GPT-OSS left whole refused as an MoE that cannot be expert-parallel yet, by the decision rather than by its spec;
- the plan a spec would supply, read without running prepare: the spec's own plan, or the one it states prepare attaches, and nothing for a model with no spec or EP section, or whose spec refuses expert parallelism;
- a decided expert-parallel load handed the spec-prepared config, and the config prepared once, not at all when handed.
Mutation-checked: skipping the spec or applying it without expert parallelism, ignoring the EP knob, letting each rank decide the spec for itself, preparing a handed config twice or not at all, ignoring the spec's plan at an EP degree of 1, ignoring the plan a spec states for prepare, and dropping the EP-unsupported branch each fail a test.
Tests the applier and decision tests already cover are dropped, and the refusal test names its three models rather than looping over whichever specs refuse. 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 GPU results here were measured on earlier revisions of this branch; this revision's GPU tests run in CI only.
On GPUs (1 node, EP4): gpt-oss-20b under torchrun stops at the plan decision with the refusal message, before any weights load. MiniMax-M3 runs on stock transformers 5.13 code (see [21/21]).

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 [21/21].
Did you get Claude approval on this PR?: ❌ Not yet.

Additional Information

Part 13 of 21 of expert-parallel PTQ for Hugging Face MoE models; merges after [12/21]. The branch's history adds a Llama-4 and a MiniMax-M3 adapter and then removes them. The Llama-4 adapter's routing was numerically wrong (it scaled expert outputs where Llama-4 scales inputs), and the M3 one only repeated what transformers 5.13 ships. Review the net diff.

Summary by CodeRabbit

New Features
- Added expert-parallel configuration support for DeepSeek-V3, Nemotron-H, Nemotron-H-Omni, and Qwen3-MoE.
- Added model registrations for MiniMax-M3-VL and Llama-4 Text.
- Model loading now applies model-specific expert-parallel settings, including nested configurations, when selecting distribution plans.
Compatibility
- GPT-OSS and Llama-4 variants are identified as unsupported for expert-parallel loading.
- Expert-parallel support requires minimum Transformers versions for applicable models.

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📝 Walkthrough

The change adds expert-parallel specifications for model plans, version requirements, and unsupported cases. It applies these specifications to Hugging Face configs during distributed plan resolution and expert-parallel loading.

Changes: Expert-parallel support

Layer / File(s)	Summary
Expert-parallel contracts and model registrations `modelopt/torch/models/specs.py`, `modelopt/torch/models/__init__.py`, `modelopt/torch/models//specs.py`, `modelopt/torch/models/llama4_text/`, `modelopt/torch/models/minimax_m3_vl/`, `modelopt/torch/models/nemotron_h_omni/`	Adds `EPSpec` to `ModelSpec` and registers expert-parallel plans, Transformers requirements, or unsupported declarations for DeepSeek-V3, GPT-OSS, Llama-4, Llama-4 text, MiniMax-M3-VL, Nemotron-H, Nemotron-H-Omni, and Qwen3-MoE.
Apply expert-parallel specifications `modelopt/torch/utils/moe.py`, `tests/unit/torch/models/test_ep_specs.py`	Adds helpers to retrieve and apply model specifications. The application checks model support and version requirements, attaches plans when absent, and runs optional preparation callbacks. Tests cover these behaviors and error cases.
Use specifications in distributed loading `modelopt/torch/utils/plugins/model_load_utils.py`, `tests/unit/torch/utils/test_load_pretrained_distribution.py`, `tests/unit/torch/utils/test_resolve_distribution_plan.py`	Applies model specifications during plan resolution and prepares configs for expert-parallel loading when needed. Tests cover plan selection, config forwarding, and rank consistency.

Priority: ➖ Normal

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Resolver as resolve_distribution_plan
  participant Ranks as Distributed ranks
  participant Support as apply_distributed_support
  participant Registry as Model spec registry
  Resolver->>Ranks: Broadcast whether to apply distributed support
  Resolver->>Support: Apply model spec to config
  Support->>Registry: Retrieve model expert-parallel spec
  Support-->>Resolver: Return prepared config
  Resolver->>Ranks: Resolve distribution plan

Merge Risk: 🟡 Moderate · up to 8decb

Providing a config for expert-parallel loading can make DeepSeek-V3 fail to load or let an unsupported model pass validation. Correct that path before merging, and add EPSpec to its source module’s public exports.

Pre-merge checks: 6

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No listed security anti-pattern was introduced. The PR changes only modelopt Python files and tests; it changes no examples or dependency manifests. No added torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), eval/exec, or # nosec was found. New Transformers loading passes the caller-controlled trust_remote_code value, whose default is False in apply_distributed_support and existing loader paths. Tests use trust_remote_code=True only as test input, which SECURITY.md excludes.

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github-actions · 2026-10-12T02:42:14Z

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shengliangxu · 2026-10-12T03:14:08Z

/ok to test 2e134ec

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shengliangxu · 2026-10-12T09:01:02Z

/ok to test fdd217a

shengliangxu · 2026-10-13T02:17:17Z

/ok to test 67f1c8f

shengliangxu · 2026-10-13T20:48:30Z

/ok to test 2f18815

shengliangxu · 2026-10-20T00:59:32Z

/ok to test 8decb14

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🟠 Major · Prepare or validate caller-supplied EP configs. · model_load_utils.py:458-459

modelopt/torch/utils/plugins/model_load_utils.py:458-459
🎯 Functional Correctness | 🟠 Major | 🏗️ Heavy lift

Prepare or validate caller-supplied EP configs.

When a caller supplies an unprepared hf_config, this condition skips apply_distributed_support. A DeepSeek-V3 config without a stock plan then reaches the EP loader’s missing-plan error. An unsupported model with a stock plan can instead bypass EPSpec.unsupported. This also affects default planning: load_pretrained retains the caller’s raw config with kwargs.setdefault("hf_config", hf_config) after resolving a prepared config. Validate every EP config, and track preparation so a config returned by resolve_distribution_plan is not prepared twice.
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Review comment at @modelopt/torch/utils/plugins/model_load_utils.py around lines
458 - 459:
The EP configuration flow around apply_distributed_support and
resolve_distribution_plan must validate caller-supplied hf_config values, not
only when hf_config is None. Track whether the configuration has already been
prepared so resolved configurations are not processed twice, while ensuring
load_pretrained receives the prepared config rather than the original raw value
through kwargs.setdefault.

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Inline comments:
Review comments at @modelopt/torch/models/__init__.py:
- Line 36: Update specs.py’s __all__ list to include the defined EPSpec symbol,
keeping the existing explicit imports in modelopt/torch/models/__init__.py
unchanged.

---

Outside diff comments:
Review comments at @modelopt/torch/utils/plugins/model_load_utils.py:
- Around line 458-459: The EP configuration flow around
apply_distributed_support and resolve_distribution_plan must validate
caller-supplied hf_config values, not only when hf_config is None. Track whether
the configuration has already been prepared so resolved configurations are not
processed twice, while ensuring load_pretrained receives the prepared config
rather than the original raw value through kwargs.setdefault.

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modelopt/torch/models/__init__.py
modelopt/torch/models/deepseek_v3/specs.py
modelopt/torch/models/llama4/specs.py
modelopt/torch/models/specs.py
modelopt/torch/utils/plugins/model_load_utils.py
tests/unit/torch/utils/test_load_pretrained_distribution.py
tests/unit/torch/utils/test_resolve_distribution_plan.py

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shengliangxu · 2026-10-20T01:13:36Z

/ok to test 6d08dec

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/ok to test d2eefcf

shengliangxu · 2026-10-20T21:10:24Z

/ok to test c34b10a

shengliangxu · 2026-10-21T17:44:38Z

/ok to test 1b6d457

shengliangxu · 2026-10-21T18:33:10Z

/ok to test 5f24160

shengliangxu · 2026-10-22T00:53:58Z

/ok to test 3ec5591

shengliangxu · 2026-10-22T01:15:12Z

/ok to test ffc20ef

The DP=EP path needs three things from a model before it can shard its experts: which modules the routed experts are and how they shard, which transformers release first ships that information, and -- for a VLM -- which nested config the plan has to be attached to. All three are facts about the architecture, so they belong with the model's other facts. So this adds an EPSpec section to ModelSpec, and apply_distributed_support, the one applier that reads it. Most models become data in the spec package they already had: deepseek_v3 carries the plan stock transformers does not ship, while qwen3_moe and gpt_oss carry only the release their own plan needs. An existing plan on the config always wins, since overwriting it would substitute our idea of the layout for the model's. Two things resisted being data and kept code, deliberately. NemotronH needs a custom parallel style, because its gate emits logits only and the group-limited top-k happens inside the MoE block -- so the usual ep_router has nothing to remap and the remap has to move to the experts' input. transformers resolves styles through a global registry rather than from config, so that registration is code, in nemotron_h/ep.py. And EPSpec.prepare is the escape hatch for checkpoints carrying their own modeling code. NemotronH-Omni is registered separately because the difference is real: its experts live in the language sub-model, and transformers aggregates a sub-model's plan up to the root WITH its module prefix -- so a plan on llm_config matches the actual parameter paths while a plan on the VLM root matches nothing at all, which fails as silence rather than as an error. A test pins both the placement and that the two spellings share one plan. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

keep_compressed asked a model's prepare hook to keep its experts packed, for a frozen speculative-decoding target. PTQ always needs the decompressed weights and hf_ptq never set it, so the knob and its pass-through go. prepare now receives ckpt_path and trust_remote_code only, which the test pins. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The applier they exercise enforces each model's transformers floor, which imports transformers. The sibling model tests skip without it the same way. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

transformers ships a base_model_ep_plan for gpt_oss, so a torchrun run took the expert-parallel path and failed only at quantization, minutes into the load. The loader drops GptOssExperts' own quantized wrapper so that the generic grouped-GEMM one takes over, and that one refuses experts stored transposed: the fused-experts export cannot write them. It would split gate and up on the wrong axis, and GPT-OSS also interleaves them. Add EPSpec.unsupported. apply_distributed_support refuses a model that names a reason, before anything is applied or loaded, and points at FSDP2. Mark gpt_oss with it. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Per-model expert-parallel support in the same structure as the model specs: one package per HF model type under modelopt/torch/models, data in specs.py, and code in ep.py only where the model genuinely needs it. These two need code. Llama-4 is the one MoE that does not follow the transformers sparse-dispatch convention -- its block routes inline and computes every expert densely, so there is no router-to-experts seam for the transport to wrap; ep.py adapts it to the ExpertsInterface contract instead. llama4_text reuses llama4's adapter and differs only in where the plan lives -- the config itself rather than a nested text config, since there is no VLM wrapper. MiniMax-M3 already follows the convention, with fused [E, out, in] expert params, but hardcodes a per-expert loop in its experts forward, so it only needs its experts class routed through the ExpertsInterface. The declarative half is covered by the spec tests. The prepare hooks patch real transformers classes, so they are exercised by the PTQ runs rather than by unit tests. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The Llama-4 adapter rewrote Llama4TextMoe.forward to dispatch its top-k through the experts interface, but it scaled each expert's output by the router score where Llama-4 scales the input, and SwiGLU does not commute with that: on a tiny config the outputs differ by up to 6.9 on values near 11. Its experts are also stored transposed, which the expert-parallel export cannot write, so the run failed at quantization anyway. Drop the adapter and refuse llama4 and llama4_text the way gpt_oss is refused: transformers ships a plan for both, which would otherwise route them into the expert-parallel path. Every transformers release with minimax_m3_vl already decorates MiniMaxM3VLExperts exactly as the M3 adapter did and ships the text config's base_model_ep_plan, and the loader selects the all-to-all experts forward after loading, so the adapter changed nothing at runtime. Keep only its transformers floor. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

nemotron_h/ep.py registered a NemotronHExpertsEP parallel style for the plan's nemotron_experts_ep entry, but load_and_prepare_ep maps that entry to torch_a2a_experts before loading, so the style never ran. Registering it imported transformers' all_reduce_forward, which 5.18 no longer has, so applying Nemotron-H's spec failed on CI's latest transformers; its _prepare_* hooks are not called from 5.16 on either. The plan moves into the spec that both spellings of the architecture share, with no prepare hook. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

transformers 5.18 added nemotron_h_omni, so its spec can no longer claim remote code; test_specs_vs_transformers fails on the latest transformers until it names the release. Expert parallelism still needs the checkpoint's code, whose llm_config the plan targets. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

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

For several models the spec is what supplies the expert-parallel plan: DeepSeek-V3 ships none of its own, so deciding without the spec would read its MoE as dense. Unless the run rules expert parallelism out (a single process, or an EP degree of 1), the model's spec is applied to the config before rank 0 builds the model to decide, and the returned config carries it. A load prepares that config once, and not at all when handed the one resolve_distribution_plan returned, since a model's preparation can patch its modeling code. A run that rules expert parallelism out applies no spec, so a model whose spec refuses expert parallelism, like GPT-OSS, still loads FSDP2-sharded. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Each rank read its own MODELOPT_EP_DEGREE to decide whether to apply the model spec, though the knobs are rank 0's. A rank whose environment ruled expert parallelism out skipped the spec rank 0 applied, loaded without the expert-parallel plan the others shard by, and raised once the mesh was up, leaving them waiting. Rank 0 decides it and sends it, as it does the plan. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

An EP degree of 1 skips the model's spec, so the guard against leaving an expert-parallel MoE whole on every rank found routed experts only through the config's own base_model_ep_plan. DeepSeek-V3, whose plan comes only from its spec, therefore loaded whole on every rank at MODELOPT_EP_DEGREE=1 with a replicated dense part, where Qwen3-MoE, which carries its plan, refused. The guard now also looks for the experts through the plan the spec would supply. The spec itself stays skipped: it refuses models that cannot be expert-parallel, which an EP degree of 1 is how to load. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

At an EP degree of 1 the guard against holding an expert-parallel MoE whole on every rank falls back on the plan the model's spec would supply, since that degree skips the spec. It read only the spec's base_model_ep_plan, so a remote-code model whose prepare attaches the plan, after patching the checkpoint's modeling, showed no routed experts and loaded whole on every rank. Running prepare there is not an option: it patches the model's code, for a run that is not expert-parallel. EPSpec.prepared_ep_plan states the plan prepare attaches, and the plan the guard reads includes it. prepare still attaches the plan itself; the spec only states it. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The refusal of an MoE held whole on every rank told every model to raise MODELOPT_EP_DEGREE above 1. GPT-OSS and Llama-4 carry plans of their own, so they reach that refusal too, but their specs refuse expert parallelism: following the advice would only have hit that refusal next. For them the error now says they cannot be quantized expert-parallel yet and points at FSDP2 alone, as their specs' own refusal does. Both messages name the ways to ask for FSDP2: --use_fsdp2 in hf_ptq, or MODELOPT_FSDP_SIZE=world. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…ided The test played rank 1 against a stand-in broadcast that answered with rank 0's decision. The pooled job that checks every rank takes rank 0's plan now covers the spec too: rank 1's own EP knob says the opposite of rank 0's, and rank 1 must apply DeepSeek-V3's spec exactly when rank 0 did, over the real broadcast, or it would load without the plan the others shard by. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

test_the_plan_a_spec_would_supply imported modelopt.torch.models and modelopt.torch.utils.moe inside the test, for no circular-import or optional-dependency reason: the module's transformers guard already precedes its other modelopt imports. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

- test_some_model_declares_expert_parallel_support goes: with no spec registered, the named-model applier and decision tests fail first. - test_a_model_type_with_no_spec_is_left_alone goes: every multi-rank dense decision test runs the same early return. - test_the_plan_is_attached_when_the_config_has_none goes: test_the_spec_is_applied_before_the_decision attaches the real deepseek_v3 plan. - The refusal test is parametrized over gpt_oss, llama4 and llama4_text by name rather than over whichever specs refuse, so a lost refusal fails it instead of shrinking it. It replaces test_models_whose_own_plan_would_misroute_them_are_refused. - The two prepare probes are one test. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

modelopt.torch.models exports EPSpec, but specs.py, where it is defined, left it out of its __all__. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

The expert-parallel route of a sharded load handed a plan but no config read the config without token, cache_dir or local_files_only, as the FSDP2 route did before. It now reads it with the Hub options the load was given. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

shengliangxu · 2026-10-22T06:47:25Z

/ok to test 242ae26

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

shengliangxu force-pushed the shengliangx/hfep-9-ep-model-specs branch from 2142693 to 2e134ec Compare October 12, 2026 02:35

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shengliangxu changed the title ~~[11/19] Describe expert parallelism in the model specs~~ [OMNIML-6076] [11/19] Describe expert parallelism in the model specs Oct 12, 2026

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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-6076] [11/19] Describe expert parallelism in the model specs~~ [OMNIML-6076] [11/20] Describe expert parallelism in the model specs Oct 13, 2026

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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-6076] [11/20] Describe expert parallelism in the model specs~~ [OMNIML-6076] [12/21] Describe expert parallelism in the model specs Oct 13, 2026

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

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shengliangxu changed the title ~~[OMNIML-6076] [12/21] Describe expert parallelism in the model specs~~ [OMNIML-6076] [13/22] Describe expert parallelism in the model specs Oct 14, 2026

shengliangxu force-pushed the shengliangx/hfep-9-ep-model-specs branch from d68e7ff to da76af0 Compare October 14, 2026 01:47

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Comment thread modelopt/torch/models/__init__.py

shengliangxu mentioned this pull request Oct 20, 2026

[OMNIML-6054] [6/21] Route load_pretrained by the distribution plan #2779

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shengliangxu added 19 commits October 22, 2026 06:39

Skip the EP spec tests without transformers

f2a77f1

The applier they exercise enforces each model's transformers floor, which imports transformers. The sibling model tests skip without it the same way. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

Cite the expert-parallel loader where it lives, in model_load_utils

dd7b443

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

List EPSpec in the model specs' public API

b4718b7

modelopt.torch.models exports EPSpec, but specs.py, where it is defined, left it out of its __all__. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

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