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Support transformers 5.15-5.18 (<5.19) and drop DBRX - #2649
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThe pull request updates Transformers compatibility for tensor-parallel quantization and training arguments. It removes built-in DBRX quantization and export support. It also adjusts loss input filtering and model examples. ChangesHugging Face quantization compatibility
Training argument and loss compatibility
Model example compatibility
DBRX export support removal
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~50 minutes Change: Feature Merge Risk: ⚪ Minimal · up to The compatibility change interprets fractional warmup steps for newer Transformers. In the verified legacy version, positive warmup steps already took precedence over the ratio, so the cited configuration does not establish a lost effective ratio. No actionable merge risk remains beyond normal checks. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 56.41% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 39 functions across 18 files. (2 skipped: 2 unsupported.) ✨ Finishing Touches 💡 1🧪 Generate unit tests (beta)
🛠️ Fix failing CI checks 💡
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## main #2649 +/- ##
==========================================
+ Coverage 69.19% 78.41% +9.21%
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Files 620 618 -2
Lines 69654 69584 -70
==========================================
+ Hits 48197 54564 +6367
+ Misses 21457 15020 -6437
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Review comments at @modelopt/torch/opt/plugins/transformers.py:
- Line 328: Update the legacy warmup-key translation so warmup_steps does not
silently overwrite an explicit warmup_ratio; define precedence or reject
conflicting values, and add a test covering both keys with different values.
Review comments at @modelopt/torch/quantization/plugins/huggingface.py:
- Line 585: Update the `tp_mesh` matching around the `_device_mesh` lookup so
multidimensional TP weights match the `"tp"` submesh by mesh dimension and rank
group rather than object identity, while preserving full-mesh matching for
one-dimensional TP.
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examples/alpamayo/qad.pyexamples/alpamayo/quantize.pyexamples/llm_qat/configs/train/finetune.yamlexamples/llm_qat/configs/train/qad_nvfp4.yamlexamples/llm_qat/configs/train/qad_scale_only.yamlexamples/llm_qat/configs/train/qad_with_learnt_amax.yamlexamples/llm_qat/configs/train/qat_nvfp4.yamlexamples/llm_qat/configs/train/qlora_nvfp4.yamlmodelopt/torch/opt/plugins/transformers.pymodelopt/torch/puzzletron/anymodel/models/qwen3_vl/qwen3_vl_model_descriptor.pymodelopt/torch/quantization/plugins/huggingface.pymodelopt/torch/quantization/utils/core_utils.pytests/gpu/torch/quantization/plugins/test_transformers_tp.pytests/unit/torch/opt/plugins/test_modelopt_arg_parser.py
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LGTM: the fixes for transformers 5.15–5.18 look correct and keep the 4.57–5.14 paths. A human needs to sign off on the justified edit to the existing test_dbrx test.
Needs action:
- Sign off on the
test_dbrxedit intests/unit/torch/quantization/plugins/test_huggingface.py. The assertions now branch on_DBRX_TRANSPOSED_EXPERTSbecause 5.15 changed DBRX's expert orientation. They still check exact equality, so the test wasn't loosened.
No action needed:
- The design-review gate fired only on directory count. This is a dependency bump with targeted compatibility fixes, and it adds no new abstraction.
- I checked the DBRX re-layout math for both orientations, and the
_translate_warmup_keyscases on 4.x, 5.0–5.14 and 5.15+. Both are correct. _forward_pre_dmcomes fromDynamicModule.convert. The newcore_utils.pycheck only matches HF parallel linears, because no other class definesenable_weight_access_and_writeback.HFParallelLinear.is_compatiblecompares meshes by identity (weight.device_mesh is tp_mesh). A TP sub-mesh of a 2-D mesh would be silently skipped. The new TP-test assertion would catch that for plain TP.- I read the tests but didn't run them. The GPU TP test was run by the author.
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DBRX support was removed in #2649; use Llama4's fused BMM experts as the example instead. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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) ### 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 ~2025 to ~1525 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 targets `main`; each later PR targets the one before it. 1. #2576 — Add model specs for llama4, qwen3_vl_moe, falcon, step3p5 and step3p7 ← **this PR** 2. #2577 — Add the `modeling_ptq.py` convention; move Nemotron-H, Falcon and Llama4 3. #2578 — Move GPT-OSS and Qwen3-VL-MoE 4. #2580 — Move the Step family (step3p5, shared by step3p7) The DBRX slice (#2579) was dropped: #2649 removed DBRX support, so there was nothing left to move. ### This PR [1/4] Registers a `ModelSpec` for each model type that gains `modeling_ptq.py` later in the series but has no spec yet. Each spec records only facts checked against transformers 4.57 and 5.14: - `llama4` and `qwen3_vl_moe`: fused MoE layout (`gate_up_proj`/`down_proj`), no gate/up pair, grouped export off. This is data only: both blocks were already detected as MoE (structurally and by name), and their expert containers are exported without name lookups. - `falcon`: dense, no sections. - `step3p5` / `step3p7`: `modeling_source="remote_code"` and intentionally no `MoESpec`. Step's expert projections sit directly on the MoE MLP with no `experts` container. Declaring the block would make `is_moe` claim it and send AWQ export into `get_experts_list`, which does not support that layout. `step3p7` gets its own package, following the `gemma4_text` / `gemma4` precedent. The exhaustive spec tables in `tests/unit/torch/models/test_model_specs.py` (`EXPECTED_MOE_LAYOUTS`, `root_class_names`) gain the two MoE rows. ### Usage No API change. The new specs are read through the existing registry, e.g. `get_spec("llama4").moe_spec`. ### Testing Each branch of the stack checked out and tested with `pytest tests/unit/torch/models tests/unit/torch/quantization tests/unit/torch/export tests/unit/recipe` on CPU (torch 2.11, transformers 5.14), on `main` at 9f902ae (#2649): 2248 passed, 7 skipped. The new block names resolve in `test_specs_vs_transformers.py`, and the step3p5/step3p7 remote-code absence checks pass. Class names were also checked against the transformers 4.57 wheel. GPU tests and the transformers 4.57 matrix 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?: ✅ Spec tables extended (`EXPECTED_MOE_LAYOUTS`, `root_class_names`). - 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 model support for Falcon, Llama 4, Qwen3-VL-MoE, Step-3.5, and Step-3.7. * Llama 4 and Qwen3-VL-MoE support includes recognition of their fused expert layers. * Falcon, Llama 4, and Qwen3-VL-MoE require Transformers 4.57 or later. Step-3.5 and Step-3.7 use remote-code modeling. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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### What does this PR do? Type of change: backward breaking (dependency floor bump), cleanup Raises the minimum transformers version from 4.57 to 5.5, so the supported range becomes `>=5.5,<5.19`. CI's `tf_min` leg moves to `transformers~=5.5.0`. It then removes the code that only existed for transformers 4.x (or for 5.x releases older than 5.5). **Why 5.5 and not higher:** TensorRT-LLM 1.3.0rc20 (CI's TRT-LLM container) pins `transformers==5.5.4`. A higher floor makes pip upgrade it inside that container. TRT-LLM then rejects the exported Qwen3-VL checkpoint (`Unsupported rotary scaling type: axial`), which failed `hf_ptq` CI on an earlier 5.8 revision of this PR. The CHANGELOG has had a "transformers 4.x support will be dropped in a future release" note since 0.46. Follow-up to #2649, which extended the supported range up to 5.18. - **Pins:** the `pyproject.toml` `hf` extra, the "not tested" warning in `modelopt/torch/__init__.py`, and `noxfile.py` `tf_min`. `tf_min` drops its `diffusers<0.40` and `datasets<5.1` (#2666) pins. Both only worked around transformers 4.57's `huggingface_hub<1.0` cap, and 5.5 allows `huggingface_hub>=1.0`. - **Removed: dead version branches.** These include: - `TRANSFORMERS_VERSION_GE_5_0` (quantization plugin) and `_TRANSFORMERS_GE_5_0` (opt plugin). - The 4.x paths in `_QuantSparseSequentialMoe`, the zero3 loader patch, the tied-weights shim, and the expert-removal hook. - The 4.x-only direction of the warmup-key translation. - The `transformers<5.3` CVE warning. - **Removed: dead workarounds.** On every supported version, these either never ran or did nothing: - `_QuantQwen3VLMoeTextExperts`. 5.5 already ships the fused `Qwen3VLMoeTextExperts` layout, so it was never registered. - The DFlash Qwen3-VL mRoPE workaround, which ran only on transformers 5.3.0, plus its tests. - `_undo_torch_init_override_by_transformers`. 5.x moved `TORCH_INIT_FUNCTIONS` out of `modeling_utils`, so this was a no-op from 5.0 on. - The Puzzletron Qwen2/Qwen3/GPT-OSS dummy-block `attention_type` copies. 5.5+ decoder layers have no `attention_type`. - `convert_file_size_to_int` import attempts (removed in 5.x). - **Unguarded imports:** these now exist in every supported version: - `Llama4TextExperts`, `FalconLinear`, `FP8Linear` and `GptOssExperts` in the quantization plugin. - `conversion_mapping` and `core_model_loading` in `model_load_utils`. - **Updated:** - Model specs clamp `min_transformers_version` to the new 5.5 floor (DeepSeek-V4 keeps 5.8), as `modelopt/torch/models/README.md` prescribes. - Library `from_pretrained`/`from_config` calls use `dtype=` instead of the `torch_dtype=` that 5.x deprecates. - `examples/puzzletron/requirements.txt` drops `transformers<5.0`, which would otherwise downgrade below the new floor. - The `warmup_ratio` comments in the gpt-oss/llm_qat configs and notebooks are gone. - **Tests:** - Tests that only ran on 4.x are rewritten or removed. `TestQuantSparseSequentialMoe` now runs on a synthetic sequential MoE block instead of 4.x's tiny Qwen3-MoE, so it runs again. - `test_peft_flow` gets a looser tolerance (`atol` 1e-2 → 3e-2). It already failed on 5.8 before this PR: 5.8 initializes the tiny GPT-OSS with ~4.5x larger logits. Comparing the merged-weight INT8 model against LoRA-on-quantized-base then exceeds 1e-2. - **Sparse-MoE calibration fix (from review):** collapsing the version branch left `_QuantSparseSequentialMoe` token forcing asserting `gate.top_k`, which broke remote-code blocks that keep `top_k` on the block or name the count `n_routed_experts`. Token forcing now resolves both structurally. It also counts the first calibration batch, which the lazy init used to drop. New layout and end-to-end `mtq.quantize` regressions cover it. **Not changed:** - `_T5QuantAttention` stays: T5 moved to the attention interface only in 5.15. - The `_hf_tp_plan` TP path stays: 5.5–5.15 still use it. - `warmup_ratio` → `warmup_steps` translation stays: 5.15 removed `warmup_ratio`. - `_QuantSparseSequentialMoe` and `register_sparse_moe_on_the_fly` stay: remote-code MoEs still use per-expert `nn.Linear`. - `_checkpoint_conversion_mapping` renames stay for remote code. - Top-level `rope_theta` stays in the EAGLE default config: the Megatron EAGLE plugin reads it. ### Usage ```bash pip install -U "nvidia-modelopt[hf]" # transformers>=5.5,<5.19 ``` ### Testing All on this host (Python 3.13, torch 2.14, 2x RTX 6000 Ada), on this branch. - **Probe of 5.5.4, 5.8.1 and 5.18.0:** every unguarded import exists. `Qwen3VLMoeTextExperts` already has the fused layout on 5.5.4. `modeling_utils.TORCH_INIT_FUNCTIONS` and `transformers.utils.convert_file_size_to_int` are gone. 5.5.4 still has `warmup_ratio`, the `_hf_tp_plan` TP path and pre-interface T5. - **5.5.4 (new floor):** `nox -s "unit-3.13(torch_214, tf_min)"` (fresh env: transformers 5.5.4, `datasets` 5.1.0, `huggingface_hub` 1.33.0): 4506 passed, 38 skipped, 0 failed. `test_transformers_tp.py` (2 GPUs) and `test_vllm_fakequant_hf_export.py`: 6 passed. - **`tests/unit` on 5.8.1 (fresh nox env):** 4502 passed, 37 skipped, 0 failed. - **`tests/unit` on 5.18.0:** 4530 passed, 9 skipped, 0 failed. - **`tests/unit/torch/quantization` after the sparse-MoE fix:** 1346 passed on both 5.8.1 and 5.18.0. The 5 new sparse-MoE tests fail on main's plugin and pass with the fix. - **GPU on 5.8.1:** `test_transformers_tp.py` (2 GPUs) and `test_vllm_fakequant_hf_export.py` pass (6 passed). - **`tests/gpu/torch/puzzletron/test_puzzletron.py` on 5.18.0:** 7 passed, 2 skipped (no `mamba_ssm`). This covers the removed Qwen2/Qwen3/GPT-OSS dummy-block overrides. - On 5.8.1 the Llama/Mistral/Qwen2/Qwen3 cases fail their `lm_loss` check, and main's code gives the same values. The expected losses are tuned for 5.18. - GPU CI installs the newest allowed transformers, so it isn't affected. - **Not run:** `test_nemotron_h_gpu_validation.py` (no `mamba-ssm` here) and the example suites. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ❌ transformers 4.57–5.4 is no longer supported. - 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?: ✅ The sparse sequential MoE tests are rewritten so they run on 5.x. - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ Backward Breaking Changes. - Did you get Claude approval on this PR?: ❌ ### Additional Information Users still pinning transformers 4.x in this repo, left as is (owners to decide): - The speculative-decoding MiniMax-M2.7 DFlash flow (`OVERRIDE_TRANSFORMERS=4.57.x`, `fsdp2_buffer_patch.py`, `tools/launcher/examples/MiniMax/MiniMax-M2.7-DFlash`). - The launcher's Nemotron-3-Nano `mbridge_prune.yaml` (`transformers<5` on `nemo:26.04`). - `examples/windows/*` (ONNX flows, `transformers<5.0` / `==4.57.3`). - `experimental/dms` (standalone, pins 4.57.3). `uv.lock` is left to the weekly relock job. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Breaking Changes** * Transformers 5.5 or later is now required; Transformers 4.x is unsupported. * Layerwise calibration now uses quantized activations from the previous layer by default. This can be configured. * **Improvements** * Updated checkpoint handling for DeepSeek-V4 indexers and unexpected weights during Hugging Face post-training quantization. * Updated Megatron distillation loss behavior and configuration. * Improved model loading and export compatibility with supported Transformers versions. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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) ### 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 targets `main`; each later PR targets the one before it. 1. #2576 — Add model specs for llama4, qwen3_vl_moe, falcon, step3p5 and step3p7 2. #2577 — Add the `modeling_ptq.py` convention; move Nemotron-H, Falcon and Llama4 ← **this PR** 3. #2578 — Move GPT-OSS 4. #2580 — Move the Step family (step3p5, shared by step3p7) 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 [2/4] Introduces `<model_type>/modeling_ptq.py` and moves the first three models there: - `nemotron_h`: `is_nemotron_h_model` / `get_nemotron_h_decoder_layers` and their layerwise-calibration registration. - `falcon`: the `FalconLinear` registration and `register_falcon_linears_on_the_fly`. - `llama4`: `_QuantLlama4TextExperts` and its registration. The registrations move in the form #2670 gave them (a top-level transformers import plus `if ... not in QuantModuleRegistry`), and `huggingface.py` drops the imports it no longer uses. It also adds the `importlib` loop in `huggingface.py` that imports each `modeling_ptq`, documents the convention in `modelopt/torch/models/README.md` and the package docstring, and adds `tests/unit/torch/models/test_modeling_ptq_registration.py`, which checks in a fresh interpreter per case that registration does not depend on which module is imported first. It also raises the `llama4`, `qwen3_vl_moe` and `falcon` specs from #2576 to the transformers 5.5 floor that #2670 set for every other spec, and drops the `qwen3_vl_moe` spec's pointer to the pre-5.12 wrapper #2670 removed. ### 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`, 2370 passed, 1 skipped. With transformers 5.5 (the new floor): `tests/unit/torch/models` plus `test_moe_linear.py` and `test_huggingface.py`, 205 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; covered by existing tests) - 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 Llama 4 expert layers. * Expanded Falcon quantization coverage to include linear layers discovered at runtime and older remote-code checkpoints. * Added Nemotron-H model detection and decoder-layer discovery for quantization. * **Bug Fixes** * Improved Qwen3-VL mixture-of-experts compatibility by applying the legacy quantization wrapper only when needed. * **Documentation** * Clarified how model-specific quantization support is loaded and where model support information belongs. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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DBRX support was removed in #2649; use Llama4's fused BMM experts as the example instead. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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DBRX support was removed in #2649; use Llama4's fused BMM experts as the example instead. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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…dels [3/4] (#2578) ### 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 and #2577 have merged; #2578 now targets `main`, and #2580 targets #2578's branch. 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 ← **this PR** 4. #2580 — Move the Step family (step3p5, shared by step3p7) 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 [3/4] Moves `_QuantGptOssExperts` and its registration into `gpt_oss/modeling_ptq.py`, adds `gpt_oss` to the import loop, and extends the registration test to it. This slice originally also moved the pre-5.12 Qwen3-VL-MoE wrapper; #2670 removed that wrapper, so that half was dropped. ### 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`, 2371 passed, 1 skipped. With transformers 5.5 (the new floor): `tests/unit/torch/models` plus `test_moe_linear.py` and `test_huggingface.py`, 206 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 GPT-OSS models. * Added support for quantizing legacy Qwen3-VL-MoE layouts, while retaining support for newer fused layouts. * Quantization support now accommodates both legacy and newer Qwen3-VL-MoE model layouts. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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DBRX support was removed in #2649; use Llama4's fused BMM experts as the example instead. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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…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>
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What does this PR do?
Type of change: new feature (dependency bump), bug fix, backward breaking (DBRX removal)
Extends the supported transformers range to
>=4.57,<5.19and moves CI'stf_latestto 5.18 (the latest release). It also fixes everything in 5.15–5.18 that breaks ModelOpt; each fix keeps 4.57–5.14 working. Separately, it removes DBRX support, and the customized-model quantization guide now uses Llama 4 as its example. Among other things, the HF side can now loadglm5_next(GLM-5.3-Flash, native since 5.16.1).pyproject.tomlhfextra, the "not tested" warning inmodelopt/torch/__init__.py, andnoxfile.pytf_latest(~=5.14.0→~=5.18.0)._hf_tp_planthat ModelOpt used to recognize TP-sharded linears. A model loaded withtp_plan="auto"was therefore quantized as if unsharded: its forward failed on mixedTensor/DTensorinputs.model._device_mesh):Shard(0)is colwise andShard(1)is rowwise. Matching the mesh (by==) keeps FSDP2 shards out. The mesh is found on any submodule, so a model held by a trainer, PEFT or a wrapper still works, and ModelOpt warns if a TP mesh exists but no linear matched.forward, whichDynamicModule.convertkeeps as_forward_pre_dm.HFParallelLinear.forwardroutes through it, and sets HF's_hf_quantized_needs_local_tpso the quantized linear runs on this rank's plain weight shard, in training as well.core_utils.pynow keys on the layer's ownenable_weight_access_and_writebackinstead of_hf_tp_plan.Llama4TextExpertsplugin (fusedtorch.bmmexperts) as its example instead of DBRX. There is a Deprecations entry in the changelog.TrainingArguments.warmup_ratioremoved (5.15, #46917):ModelOptArgParsernow accepts either spelling in YAML configs and translates to whichever the installed transformers supports.warmup_ratiobecomeswarmup_stepson 5.15+, where a value below 1 is a ratio. A fractionalwarmup_stepsbecomeswarmup_ratioon 4.x, wherewarmup_stepsis an int.llm_qattrain configs now usewarmup_steps. They failed to parse on 5.15+.examples/alpamayo/qad.pypicks the supported key.Qwen3VLMoeVisionRotaryEmbeddingnow takes the vision config instead ofdim, so the descriptor builds it from the config there. The old call raisedAttributeError: 'int' object has no attribute 'rope_parameters'.crop()calls are deprecated (#47720).skip_logits=Trueto eval inputs whenuse_liger_kernelis set.compute_loss_funcmakes the Trainer poplabels. So the student's and the teacher's Liger forwards raisedskip_logits is True, but labels and shift_labels are None.llm_qatQAT, QAD and QLoRA in CI. The flag is now dropped in both paths.Checked, no change needed:
ALL_ATTENTION_FUNCTIONS, so it registers with the generic_QuantAttention. FP8 KV-cache scales calibrate on 5.18, and_T5QuantAttentionstays for 4.57–5.14.indexed_attention. ModelOpt never compares these strings.min_pixels/max_pixels(#49021): ModelOpt passes them only tofrom_pretrained, which is still supported.update_candidate_strategy, anduse_mamba_kernels.Usage
Testing
All on this host (Python 3.13, torch 2.14). The 3.12 nox session can't start here because the system Python has a libffi/
_ctypesmismatch, which is unrelated.nox -s "unit-3.13(torch_214, tf_latest)"on 5.18, before the fixes: 4532 passed, 1 failed (test_dbrx, whose model is now removed).tests/unit/torch/quantization+tests/unit/torch/opton 5.18: 1414 passed, 8 skipped.tests/unit/torch/quantization/plugins/test_huggingface.py: passes on 5.14.1 and 5.18.0.tests/gpu/torch/quantization/plugins/test_transformers_tp.py(2 GPUs): passes on 5.14.1, 5.16.1 and 5.18.0.aten.mm.default got mixed torch.Tensor and DTensor.tests/examples/llm_qat/test_llm_qat.pyon 5.18 (2 GPUs): QAT on DDP, QAD on FSDP2, and QLoRA pass.attn_implementation: sdpalocally, becauseflash-attndoesn't build on this host.skip_logitsfix, QAD on FSDP2 reproduces the CI error on both ranks.test_transformers_tpwrapped-model case: fails with the old top-level mesh lookup and passes with the fix, on 5.14.1 and 5.18.0.tests/unit/torchon 5.18: 3355 passed, 15 skipped. Thetest_dbrxcases are gone, the spec tests that used DBRX now useqwen3_5_moe, and the DBRX cases in the export-registry test are dropped._QuantLlama4TextExpertsinserts the same 4 quantizers on a tinyLlama4TextExpertsand matches the shipped plugin's FP8 output exactly.test_yaml_warmup_spelling_follows_transformerscovers both translation directions.llm_qat/configs/train/qat_nvfp4.yamland a legacywarmup_ratio: 0.05YAML throughllm_qat'sTrainingArgumentson 5.18 giveswarmup_steps 0.05for both.ARGUMENTS.mdregenerates unchanged. Its pre-commit hook couldn't run on this host because the hook'suvenv has no torch, so I generated it manually and diffed.init_rotary_embeddingreproduces the model's own visioninv_freqon both 5.14.1 and 5.18.0.tf_latestpin.Before your PR is "Ready for review"
llm_qatconfigs move fromwarmup_ratiotowarmup_steps, butModelOptArgParserstill acceptswarmup_ratioon every version.CONTRIBUTING.md: N/AAdditional Information
uv.lockis left to the weekly relock job. Example-specific pins (examples/speculative_decoding,examples/puzzletron,examples/windows/*) are left as they are; they pin for their own reasons.Follow-up opportunities from the new releases, not in this PR:
glm5_next/step3p7models in the recipe tests, which would need a skip on the 4.57 CI leg.hf_ptq.hf_ptqcalibration speed now that linear-attention kernels are opt-in (#47630).🤖 Generated with Claude Code
Summary by CodeRabbit