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[OMNIML-6032] Deprecate TensorRT-LLM model_type in favor of Hugging Face model_type - #2569
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 8 remain after this review. 📝 WalkthroughWalkthroughTensorRT-LLM checkpoint export can detect decoder types from model classes when ChangesModel type and export updates
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Refactor Sequence Diagram(s)sequenceDiagram
participant Caller
participant export_tensorrt_llm_checkpoint
participant get_decoder_type
Caller->>export_tensorrt_llm_checkpoint: Call with decoder_type omitted
export_tensorrt_llm_checkpoint->>get_decoder_type: Resolve decoder type from model class
get_decoder_type-->>export_tensorrt_llm_checkpoint: Return decoder type or None
export_tensorrt_llm_checkpoint-->>Caller: Export, warn and use unknown class-name type, or raise ValueError
Merge Risk: 🔵 Low · up to Exports that omit decoder_type for unrecognized model classes can still fail despite the documented generic fallback. Passing an explicit type provides a workaround, so this is a bounded risk to the new export behavior. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 54.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 50 functions across 12 files. (1 skipped: 1 unsupported.)
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get_model_type and MODEL_NAME_TO_TYPE return TensorRT-LLM model names, which only the deprecated TensorRT-LLM checkpoint export consumes. - Move the name table into modelopt.torch.export.trtllm.decoder_type. - export_tensorrt_llm_checkpoint / torch_to_tensorrt_llm_checkpoint now detect decoder_type when omitted, falling back to the generic decoder export for unrecognized Hugging Face models as hf_ptq did before. - get_model_type and MODEL_NAME_TO_TYPE emit a DeprecationWarning and will be removed in 0.49.0 together with the TensorRT-LLM export. - examples/hf_ptq keys model-specific behavior on the root model's model_type via modelopt.torch.models.hf_model_type. A VLM's language model reports its own sub-config type, which varies across transformers versions, so it is no longer used. The DeepSeek generation skip now also covers kimi_k2/kimi_k25, DeepSeek-V3 architectures under their own model_type. - The HF export's Whisper and Nemotron VL checks, the Step-family check in the quantization plugin and the Qwen2.5-Omni check in the llama_factory example use the root HF model_type instead of class names or inline getattr chains. - The AutoQuantization notebook passed model_type="llama" to export_tensorrt_llm_checkpoint, whose parameter is decoder_type, so its default tensorrt_llm export raised TypeError. Drop the argument and default EXPORT_FMT to hf. - The model_type recipes README says to use the top-level model_type for multimodal models, matching the existing directories. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2569 +/- ##
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Inline comments:
Review comments at @CHANGELOG.rst:
- Line 75: Update the changelog entry for `get_model_type` and
`MODEL_NAME_TO_TYPE` to use no more than two sentences: combine the deprecation
and migration guidance in the first, then summarize
`export_tensorrt_llm_checkpoint` decoder inference and the `examples/hf_ptq`
change in the second.
Review comments at @modelopt/torch/export/trtllm/model_config_export.py:
- Line 173: Update the architecture resolution before the
MODEL_NAME_TO_HF_ARCH_MAP lookup so a missing config.architectures does not
produce an unknown:<model class> key that raises KeyError. In the decoder_type
fallback, resolve an architecture accepted by TensorRT-LLM’s MODEL_MAP or use an
explicit generic fallback before the lookup.
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CHANGELOG.rstexamples/hf_ptq/README.mdexamples/hf_ptq/example_utils.pyexamples/hf_ptq/hf_ptq.pyexamples/hf_ptq/notebooks/3_PTQ_AutoQuantization.ipynbexamples/llm_qat/llama_factory/llama_factory.pymodelopt/torch/export/model_utils.pymodelopt/torch/export/trtllm/decoder_type.pymodelopt/torch/export/trtllm/model_config_export.pymodelopt/torch/export/unified_export_hf.pymodelopt/torch/quantization/plugins/huggingface.pymodelopt_recipes/model_type/README.mdtests/examples/hf_ptq/test_hf_ptq_model_type.pytests/examples/hf_ptq/test_hf_ptq_vision_quantization.pytests/unit/torch/export/trtllm/test_trtllm_decoder_type.py
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Claude review — 2 IMPORTANT, 2 SUGGESTION, 0 CRITICAL
Scope: full review (trigger comment carried no scoping instructions). All 15 changed files considered; modelopt/ (5 files) and examples/ (5 files) read with surrounding context, tests/ (3 files) read from the diff, CHANGELOG.rst / modelopt_recipes/model_type/README.md skimmed.
Assessment
The core premise is right and unusually well-researched: keying examples/hf_ptq on the root config.model_type rather than a sub-model's, with the transformers 4.49-vs-5.14 survey in the PR body to back it, is the correct call. I traced every model_type consumer in hf_ptq.py (lines 267, 688, 909, 953, 1074, 1082, 1086, 1163, 1206, 1337, 1461) against the old TRT-LLM name it used to receive, and the old→new mapping is behavior-preserving in each case, including the non-obvious ones: MT5ForConditionalGeneration previously resolved to the TRT-LLM name "t5" and so already took the enc-dec path, which is exactly why adding mt5/umt5/mbart to is_enc_dec is a no-op rather than a widening. llama4_text is a real root type (Llama4ForCausalLM uses Llama4TextConfig), not defensive padding. The MODEL_NAME_TO_TYPE alias returns the same dict object, so the one plausible downstream use — mutating the table to register a custom model — keeps working, and the module-level __getattr__ keeps the DeprecationWarning off the package-import path since the name was never in __all__.
Two issues are worth fixing before merge; neither is in the refactor's main line.
IMPORTANT
1. unified_export_hf.py:512 — Nemotron-VL detection now keys on config.model_type, against the repo's own convention. The two other Nemotron detectors (model_utils.py:116, example_utils.py:252) both key on config.architectures, as does the is_multimodal_model gate immediately above this line. For remote-code VLMs architectures is the field the loader dispatches on and is always present; model_type is free-form. If one config's model_type lacks nemotron (or is absent — hf_model_type returns None), the branch is skipped and the fallback dummy-forwards the full VLM, which is the pixel_values crash the branch exists to prevent. The PR body's hub survey covers today's checkpoints; the concern is the weaker invariant going forward. Switching to architectures is a two-line change and makes the three call sites agree.
2. model_config_export.py:167-173 — the new "Required for Megatron-Core models" docstring is unenforced, and detection cannot satisfy it. get_decoder_type(GPTModel) returns None (no table key is a substring of gptmodel), so the value becomes unknown:GPTModel; architecture stays "" because it is only filled from model.config.architectures, which Megatron models lack; MODEL_NAME_TO_HF_ARCH_MAP[decoder_type] then raises KeyError, which the try at line 545 swallows into a generic "Cannot export" warning that never names the missing argument. A caller who previously got a clear TypeError at the call site now pays a full weight-conversion pass for an opaque failure. Relatedly, when architecture does resolve, the checkpoint is written with "decoder": "unknown:<Class>" (tensorrt_llm_utils.py:175), which TRT-LLM cannot consume — so "Continue exporting..." yields an unloadable checkpoint rather than a working generic export. Raising when detection fails and the model has no config.architectures preserves the documented contract and confines the fallback to cases where it actually works. This overlaps CodeRabbit's line-173 comment; the Megatron enforcement angle and the invalid "decoder" field are the parts it did not cover.
SUGGESTION
hf_ptq.py:909— refusal message still reads "(t5/bart/whisper)" afteris_enc_decgrew to six types.example_utils.py:903— the T5 NaN and BART meta-tensor load workarounds at lines 707/711 still exact-match"t5"/"bart", so MT5/MBart are recognized as enc-dec but not covered by the architecture-level workarounds they share. Pre-existing, optional here.
Not flagged, checked and clear
mono_quantize's unused model_type parameter (pre-existing, outside the diff); the post_quantize assert preview_input_ids is not None (holds — pre_quantize only returns None on the specdec_offline_dataset path, which early-exits above); the lazy .trtllm.decoder_type import in model_utils (deferred into the function body, so no cycle with modelopt.torch.export); assert model_type != "dbrx" (HF type is also dbrx); the whisper change at line 501 (WhisperConfig.model_type is a transformers constant); and the deliberately-unchanged dataset_utils.model_type_is_enc_dec, whose divergence the existing comment already explains.
Risk
Moderate. The deprecation shims and the examples/hf_ptq refactor are low-risk and well-tested. Risk concentrates in the two items above, both on paths the PR's own Testing section lists as not yet exercised on GPU (Nemotron-VL export requantization, int8_smoothquant TRT-LLM export fallback).
…cating-trtllm-model_type Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> # Conflicts: # examples/hf_ptq/hf_ptq.py
- export_tensorrt_llm_checkpoint: when decoder_type cannot be detected, raise a clear ValueError unless the model has config.architectures for the generic decoder path. A Megatron-Core caller that omits decoder_type otherwise failed late with KeyError: 'unknown:GPTModel'. - requantize_resmooth_fused_llm_layers: detect Nemotron VL from config.architectures, like is_multimodal_model and is_nemotron_vl, since model_type is free-form for these remote-code models. - Drop the stale "(t5/bart/whisper)" from the layerwise-export refusal label; is_enc_dec also covers mt5, umt5 and mbart. - Shorten the CHANGELOG entry to two sentences. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Changes requested: encoder-decoder previews now truncate generated answers, and Nemotron-VL export detection loses its class-name fallback.
Needs action:
- Separate preview decoding from export routing in
examples/hf_ptq; preserve encoder-decoder decoding for LongT5, T5Gemma and PLBart while keeping their intended HF export. - Preserve Nemotron-VL requantization when
config.architecturesis unset inunified_export_hf.py, and add a regression exercising the language-model-only forward. - Add focused Whisper dummy-forward and root-versus-extracted-VLM model-type regressions; current tests do not exercise those changed export/selection paths.
- Move the new local imports in
model_utils.pyto module scope, or document a concrete circular-import reason.
No action needed:
- The design reuses
hf_model_type; the PR body justifies retaining legacy TRT-LLM detection instead of extending the HF registry. - Existing vision-test edits follow the changed extraction contract without weakening assertions. New headers match
LICENSE_HEADER.
- hf_ptq: separate the preview decode from TensorRT-LLM export routing. is_trtllm_enc_dec_export(model_type) keeps the exporter's families (t5, mt5, umt5, bart, mbart, whisper); generate_excludes_prompt(model) uses the root config's is_encoder_decoder, minus DiffusionGemma. LongT5 and PLBart matched the legacy t5/bart names and decoded whole; keying both on the export list sliced a prompt-length prefix off their answers. T5Gemma, which the legacy table matched as gemma, now decodes whole too. - unified_export_hf: lift the requantization dummy forward into _llm_dummy_forward, and detect Nemotron VL from config.architectures or the class name, so a model built from a config without architectures keeps the language-model-only forward. - model_utils: document why the deprecation shims import .trtllm lazily (trtllm -> model_config_export -> quant_utils -> model_utils cycle). - Tests: preview decode with an answer shorter than the prompt, Whisper and Nemotron VL dummy forwards (architectures set and unset), and load_model keeping the root model_type when it extracts a VLM's language model. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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@cjluo-nv thanks for the review. All four action items are addressed in a5dd506:
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…cating-trtllm-model_type
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LGTM: prior correctness concerns are resolved; human sign-off remains for the justified existing vision-test update.
Needs action:
- Confirm the justified update to
test_hf_ptq_vision_quantization.py: it follows the extraction return contract and adds root-model-type coverage without weakening existing assertions.
No action needed:
- ✔️ Resolved the preview truncation, Nemotron-VL class-name fallback, missing Whisper/VLM regressions, early decoder-type validation, and documented circular imports.
- The design reuses
hf_model_typerather than adding another registry; retaining legacy detection beside the deprecated exporter is justified. - New file headers match
LICENSE_HEADER. The pre-existing T5/BART loading workarounds can remain a separate follow-up. - Tests were inspected, not executed in this review.
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Confirmed: the |
shengliangxu
changed the title
…dels [4/4] (#2580) ### What does this PR do? Type of change: Refactor ### Summary of the series Model-specific PTQ modeling moves out of `modelopt/torch/quantization/plugins/huggingface.py` into the per-model packages under `modelopt/torch/models/<model_type>/modeling_ptq.py`, next to each model's `specs.py`. By the end of the series `huggingface.py` shrinks from ~1880 to ~1500 lines and keeps only the code shared across models: sequential/fused MoE auto-detection, attention, `FP8Linear`, `CompressedLinear`, parallel linears, and the transposed-quantization helpers that gpt_oss and llama4 share. Model types that gain PTQ modeling but had no `ModelSpec` get one. - Class and function bodies move verbatim. The only rewrites: on-the-fly callbacks are added to `CUSTOM_MODEL_PLUGINS` by their own module, and `is_homogeneous_hf_model` imports `is_nemotron_h_model` lazily. - `huggingface.py` imports every `modeling_ptq` from an explicit list. The list sits after the generic wrappers these modules build on and before the homogeneous decoder discoverer is registered, so Nemotron-H's more specific discoverer still matches first. - Each package's `__init__.py` keeps importing only `specs`, so `import modelopt.torch.models` does not pull in quantization or transformers. - No public API or quantization behavior changes; only private (`_`-prefixed) names change module. Merge in order. #2576, #2577 and #2578 have merged; this last slice, #2580, now targets `main`. 1. #2576 — Add model specs for llama4, qwen3_vl_moe, falcon, step3p5 and step3p7 (merged) 2. #2577 — Add the `modeling_ptq.py` convention; move Nemotron-H, Falcon and Llama4 (merged) 3. #2578 — Move GPT-OSS (merged) 4. #2580 — Move the Step family (step3p5, shared by step3p7) ← **this PR** The DBRX slice (#2579) was dropped: #2649 removed DBRX support, so there was nothing left to move. Likewise, the Qwen3-VL-MoE half of [3/4] was dropped: #2670 removed its pre-5.12 wrapper. ### This PR [4/4] Moves `_QuantMoELinear`, `_is_expert_indexed_moe_linear`, `_is_step_family_model`, `register_moe_linear_on_the_fly` and `_reconstruct_fused_moe_linear` into `step3p5/modeling_ptq.py`, which also serves `step3p7`. This carries over #2569's switch of `_is_step_family_model` to `hf_model_type`; `huggingface.py` drops its now-unused `hf_model_type` and `re` imports. Export (`hf_export_handlers.py`, `layerwise_export.py`, `unified_export_hf.py`, `unified_export_hf_streaming.py`) imports these names from the new module. The imports stay deferred, because `modelopt.torch.quantization` imports `modelopt.torch.export` and importing at module scope would risk an import cycle; the comments now give that reason. The PTQ skill references (`unsupported-models.md`, `checkpoint-validation.md`) now point agents at `modelopt/torch/models/<model_type>/modeling_ptq.py` for model-specific patches. Rebasing onto #2649 also replaces the README's DBRX example of a model-specific wrapper with Llama4's fused BMM experts. `step3p7` also gets its own `modeling_ptq.py`, which imports the Step-3.5 module: Step-3.7 remote-code checkpoints use the same `MoELinear`, and today they are covered only because the HF plugin imports every `modeling_ptq`. With its own module, a future loader that imports PTQ modeling by model type finds it too. ### Usage No API change. To add PTQ support for a new model, put its wrapper in `modelopt/torch/models/<model_type>/modeling_ptq.py` and add `<model_type>` to the import loop at the end of `quantization/plugins/huggingface.py`. ### Testing Each branch of the stack checked out and tested on `main` at 90ba9fb, CPU, torch 2.11. With transformers 5.18: `pytest tests/unit/torch/models tests/unit/torch/quantization tests/unit/torch/export tests/unit/recipe`, 2372 passed, 1 skipped. With transformers 5.5 (the new floor): `tests/unit/torch/models` plus `test_moe_linear.py` and `test_huggingface.py`, 207 passed, 1 skipped. GPU tests are left to CI. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ Only private (`_`-prefixed) names change module; public API and quantization behavior 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?: N/A (verbatim move; existing tests re-pointed) - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A (internal refactor) - Did you get Claude approval on this PR?: ❌ ### Additional Information Part of a 5-PR stack; see the series list above. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added post-training quantization support for Step-family models with expert-indexed MoE layers. * Checkpoint exports preserve the original layout of quantized expert weights and scales. * **Documentation** * Updated MoE architecture examples and guidance for configuring model-specific quantization support, including Step model revisions. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
2 participants
What does this PR do?
Type of change: Deprecation / refactor
get_model_type()andMODEL_NAME_TO_TYPEinmodelopt.torch.export.model_utilsreturn TensorRT-LLM model names (gpt,llama,qwen,deepseek, …) derived by substring-matching the model class name. The only real consumer is the TensorRT-LLM checkpoint export, which is already deprecated for removal in 0.49.0.examples/hf_ptqused these names for all of its model-specific behavior. This PR switches it to the Hugging Facemodel_typeand deprecates the TRT-LLM names.Library
modelopt/torch/export/trtllm/decoder_type.py, next to the TRT-LLM exporter it serves. It goes away with that exporter in 0.49.0.export_tensorrt_llm_checkpoint/torch_to_tensorrt_llm_checkpointacceptdecoder_type=Noneand detect it from the model class. An unrecognized HF model falls back to the generic decoder export with a warning, which is whathf_ptqdid before, so no caller has to know TRT-LLM names.get_model_typeandMODEL_NAME_TO_TYPEkeep working but emit aDeprecationWarning(removal in 0.49.0).examples/hf_ptqkeys everything on the root model'sconfig.model_type, via the existing library helpermodelopt.torch.models.hf_model_type(already used by export and the model-spec registry).model_typewith the extracted VLM language model's type. A sub-model reports its own sub-config type, and those are not stable. Checking transformers 5.14 configs, 112 of 113 VLMs with atext_configgive the language model a different type from the root:qwen3_vl→qwen3_vl_text,mistral3→mistral,llava→llama. Transformers 4.49 had no separate language model forqwen2_vl. The root type is stable, because it is theconfig.jsonkey thatAutoConfigdispatches on. The--recipepath already used the root type.t5,mt5,umt5,bart,mbartandwhisper. This matches what the old class-name match routed there.deepseek*andkimi_k2*. Kimi-K2 (kimi_k2,DeepseekV3ForCausalLM) and Kimi-K2.5 (kimi_k25) are DeepSeek-V3 architectures under their ownmodel_type, and the old class-name match skipped both.Other HF model_type checks in the library now use the root type too.
requantize_resmooth_fused_llm_layers(HF export) kept a localmodel_typethat was really the lowercased class name. Its Whisper and Nemotron-VL checks now use the HFmodel_type. Every Nemotron-VL checkpoint (NemotronH_Nano_VL_V2,Llama_Nemotron_Nano_VL,nemotron_parse) hasnemotronin itsmodel_type, matched case-insensitively as before. The Step-family check in the quantization plugin and the Qwen2.5-Omni check in thellama_factoryexample usehf_model_typeinstead of inlinegetattrchains.Fixes and docs
model_type="llama"toexport_tensorrt_llm_checkpoint, whose parameter isdecoder_type, so its defaulttensorrt_llmexport raisedTypeError. It now omits the argument and defaultsEXPORT_FMTtohf.modelopt_recipes/model_type/README now says to name directories by the top-levelmodel_type, which the existing directories already follow, rather than a multimodal model'stext_config.model_type.Deliberately unchanged:
dataset_utils.model_type_is_enc_decdecides how to call the module being calibrated (generate()or a plain forward), and that module can be a sub-model. It matches class names broadly (LongT5, T5Gemma, PLBart), and moving it to a list of HF types would change calibration.config.model_typedirectly.hf_model_typealso accepts a model, which it detects by looking for a.configattribute; that adds nothing when given a config.Behavior changes (intentional):
NemotronH_Nano_VL_V2) now runs the Nemotron-VL preview instead of thenemotron_hgeneration skip. Both generation helpers there catch their own exceptions, so the worst case is a logged failure.t5/bartby substring match. They now use the unified HF export.Usage
Testing
pytest tests/unit/torch/{models,export,speculative,quantization/plugins} tests/unit/torch/puzzletron/test_resolve_descriptor_caching.py tests/unit/recipe/test_step3p7_recipes.py: 771 passed (CPU).pytest tests/examples/hf_ptq tests/unit/recipe, excluding the GPU/model-download suites: 570 passed (CPU).config.jsonchecks (DeepSeek-V3, Kimi-K2/K2.5, Moonlight, Nemotron-H and the Nemotron VL checkpoints), and a comparison of transformers 4.49 with 5.14, for the root vs sub-configmodel_typefindings above.tests/unit/torch/export/trtllm/test_trtllm_decoder_type.py: decoder-type detection, both deprecation warnings, and the exporter's detection and fallback.tests/examples/hf_ptq/test_hf_ptq_model_type.py: the generation-skip rule, includingkimi_k2andkimi_k25, and the encoder-decoder list.pre-commit(ruff, mypy, bandit, …) clean.hf_ptqruns (DeepSeek/Kimi skip, Nemotron-H VL preview,int8_smoothquantTRT-LLM export fallback) and the Whisper / Nemotron-VL export requantization path.Before your PR is "Ready for review"
get_model_type/MODEL_NAME_TO_TYPEstill work, with aDeprecationWarning.decoder_typebecomes optional.CONTRIBUTING.md: N/AAdditional Information
Follows the 0.48.0 deprecation of the TensorRT-LLM checkpoint export. Both are removed together in 0.49.0.
Summary by CodeRabbit
config.json.