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Require transformers>=5.5 and remove transformers 4.x code paths - #2670
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Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughThe changes raise the minimum Transformers version to 5.5 and remove or revise version-specific compatibility paths across model loading, quantization, pruning, and speculative decoding. Examples, support notes, and tests are updated to match these changes. ChangesTransformers 5.5 Compatibility
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~60 minutes Change: Other Suggested reviewers: Merge Risk: ⚪ Minimal · up to No confirmed issue blocks merging. Compatibility of the quantization plugin’s imports with Transformers 5.5 remains unverified. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
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Changes requested: the sequential-MoE cleanup removes structural fallbacks that remain necessary for supported remote-code models.
Needs action:
- Fix
_QuantSparseSequentialMoe.forwardto support block-leveltop_kand alternate expert-count attributes; add calibration regressions for those detected layouts (see inline comment). - Restore end-to-end sequential-MoE coverage using
mtq.quantizeon a synthetic block, verifying token forcing, expert coverage, and restored routing rather than only setting_if_calibmanually.
No action needed:
- The dependency-floor design reasonably favors removing obsolete guards over retaining version checks; existing structural detection remains necessary for remote code. No new subsystem is introduced.
- DFlash workaround removal, obsolete skipped cases, warmup assertions, and PEFT tolerance changes are justified by the floor bump and documented numerical differences.
- Tests were inspected, not executed.
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Review comments at @modelopt/torch/quantization/plugins/huggingface.py:
- Around line 740-743: Update the MoE calibration logic in forward() to use the
gate as the top_k owner when it provides top_k, and otherwise use the block
itself. Read and update top_k through that owner, derive num_experts from it
with the experts collection as fallback, and restore the original top_k after
the forward call.
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CHANGELOG.rstexamples/alpamayo/qad.pyexamples/gpt-oss/configs/sft_full.yamlexamples/gpt-oss/configs/sft_lora.yamlexamples/gpt-oss/qat-finetune-transformers.ipynbexamples/hf_ptq/README.mdexamples/llm_qat/notebooks/QAT_QAD_Walkthrough.ipynbexamples/puzzletron/requirements.txtmodelopt/torch/__init__.pymodelopt/torch/export/layerwise_export.pymodelopt/torch/export/model_utils.pymodelopt/torch/export/unified_export_hf_streaming.pymodelopt/torch/models/README.mdmodelopt/torch/models/deepseek_v3/specs.pymodelopt/torch/models/gemma/specs.pymodelopt/torch/models/gemma2/specs.pymodelopt/torch/models/gemma3/specs.pymodelopt/torch/models/gemma4/specs.pymodelopt/torch/models/gemma4_text/specs.pymodelopt/torch/models/gpt_oss/specs.pymodelopt/torch/models/llama/specs.pymodelopt/torch/models/minimax/specs.pymodelopt/torch/models/mixtral/specs.pymodelopt/torch/models/nemotron/specs.pymodelopt/torch/models/nemotron_h/specs.pymodelopt/torch/models/phimoe/specs.pymodelopt/torch/models/qwen2_moe/specs.pymodelopt/torch/models/qwen3/specs.pymodelopt/torch/models/qwen3_5_moe/specs.pymodelopt/torch/models/qwen3_moe/specs.pymodelopt/torch/models/qwen3_next/specs.pymodelopt/torch/opt/plugins/huggingface.pymodelopt/torch/opt/plugins/transformers.pymodelopt/torch/prune/importance_hooks/expert_removal_hooks.pymodelopt/torch/puzzletron/anymodel/models/__init__.pymodelopt/torch/puzzletron/anymodel/models/gpt_oss/gpt_oss_model_descriptor.pymodelopt/torch/puzzletron/anymodel/models/qwen2/qwen2_model_descriptor.pymodelopt/torch/puzzletron/anymodel/models/qwen3/qwen3_model_descriptor.pymodelopt/torch/quantization/plugins/huggingface.pymodelopt/torch/speculative/eagle/default_config.pymodelopt/torch/speculative/plugins/hf_dflash.pymodelopt/torch/speculative/plugins/hf_eagle.pymodelopt/torch/speculative/utils.pymodelopt/torch/utils/plugins/model_load_utils.pynoxfile.pyplugins/modelopt/skills/ptq/references/unsupported-models.mdpyproject.tomltests/_test_utils/examples/models.pytests/examples/hf_ptq/test_llm_ptq.pytests/gpu/torch/export/test_vllm_fakequant_hf_export.pytests/unit/torch/export/test_fsdp2_parallel_export.pytests/unit/torch/opt/plugins/test_modelopt_arg_parser.pytests/unit/torch/opt/plugins/test_transformers_save_load.pytests/unit/torch/quantization/plugins/test_attention_quant.pytests/unit/torch/quantization/plugins/test_fused_experts.pytests/unit/torch/quantization/plugins/test_peft.pytests/unit/torch/quantization/plugins/test_sparse_sequential_moe.pytests/unit/torch/speculative/plugins/test_fakebase.pytests/unit/torch/speculative/plugins/test_hf_dflash.pytests/unit/torch/utils/test_model_load_utils.py
💤 Files with no reviewable changes (7)
- examples/puzzletron/requirements.txt
- modelopt/torch/puzzletron/anymodel/models/init.py
- modelopt/torch/puzzletron/anymodel/models/qwen2/qwen2_model_descriptor.py
- tests/unit/torch/speculative/plugins/test_hf_dflash.py
- modelopt/torch/puzzletron/anymodel/models/qwen3/qwen3_model_descriptor.py
- tests/_test_utils/examples/models.py
- modelopt/torch/puzzletron/anymodel/models/gpt_oss/gpt_oss_model_descriptor.py
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…og entry Token forcing now finds top_k on the gate or the block and the expert count from num_experts/n_routed_experts/len(experts), so every layout the detection accepts calibrates (remote-code blocks asserted on gate.top_k). It also counts the first calibration batch, which the lazy init used to drop. Adds layout and end-to-end mtq.quantize regressions. Moves the transformers floor entry to the top of 0.48.0's breaking changes (it had landed in 0.46.0) and drops stray blank lines there. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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TensorRT-LLM 1.3.0rc20 pins transformers==5.5.4; a 5.8 floor upgrades it to 5.18 in that container, after which TRT-LLM rejects the exported Qwen3-VL rope config. Every 4.x removal in this PR is still dead on 5.5.4, so only the pins and spec clamps move. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Nudge: prior MoE blockers are resolved, but new-floor validation is pending and embedded review-scope directives require human attention.
Needs action:
- Finish and report the Transformers 5.5.4 unit and TP/vLLM runs; confirm the TensorRT-LLM
hf_ptqregression passes with the revised floor. - Confirm full-PR review coverage: untrusted author comments direct the reviewer to skip tests and other changed files; those scope-changing directives were ignored.
No action needed:
- ✔️ Resolved since the last review: block-level
top_k, alternate expert counts, and end-to-endmtq.quantizecoverage, including first-batch token counts and restored routing. - The dependency floor reasonably replaces obsolete
packaging.versionguards while retaining structural detection viahasattr; no second subsystem is introduced. - Test rewrites/removals are justified by unsupported-version cleanup; the PEFT tolerance change has a documented numerical rationale.
- Tests were inspected, not executed.
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LGTM. Thanks @kevalmorabia97
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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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…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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Step-3.7 remote-code checkpoints use the same expert-indexed MoELinear as Step-3.5, handled by step3p5/modeling_ptq.py. It was covered only because the HF plugin imports every modeling_ptq; a loader that imports PTQ modeling by model type would have found nothing for step3p7. Add step3p7/modeling_ptq.py importing the Step-3.5 module, list step3p7 in the plugin's import loop and the registration test, and say so in the models README (also correcting its stale try/except wording from before #2670). 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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Oct 10, 2026
…s reads it, family-wide DSpark already projected its teacher this way; the rest of the DFlash family did not. DFlash's self-logit distillation (on by default) rebuilt [B, seq, vocab] base logits every step -- offline/streaming by running the whole captured final hidden through lm_head, online through the CausalLM forward -- then gathered the N * block_size rows it uses out of them. LiLiCorr's distractor penalty read k candidate logits per slot out of that same full tensor. Now there is one way in: - DFlashBaseModelOutput carries the base final hidden (and whether it was captured before the final norm), never logits built from it; from_offline_dict loses need_logits, defer_lm_head and base_model_lm_head. - HFDFlashModel._base_outputs builds it for the whole family: replayed offline, and online through the inner model only, as DSpark and Domino already did, so text batches never run the base lm_head. Multimodal batches still need the top-level forward and keep its logits as the teacher, since hidden_states[-1] is not lm_head's input on every transformers version (Qwen3-VL on 4.57). - HFDFlashModel._teacher_logits gathers the requested rows, re-applies the base final norm when the capture was pre-norm, and projects just those rows -- or, given token_ids, just those vocab entries, which is all LiLiCorr's penalty needs. DFlash KD (and DFlash2 through the shared loss), DSpark and LiLiCorr all read the teacher through it; _compute_loss takes it as a DFlashBaseModelOutput instead of a logits tensor. Domino reads no base distribution and is unchanged. One behavior change: online, the teacher used to be the CausalLM forward's logits, which for Gemma-family bases include final_logit_softcapping; it is now lm_head on the final hidden, which is what offline/streaming training and DSpark have always used. Bases without softcapping get the same teacher up to matmul rounding. Tested: tests/unit/torch/speculative 369 passed on transformers 5.12 and the affected files 156 passed on 4.57; the speculative GPU tests 15 passed. New tests pin the teacher against the full-sequence projection (post- and pre-norm capture, token_ids, handed-over logits) and that a training step never feeds lm_head the whole sequence; each fails when its property is broken. On JHB, Gemma-4-E4B DSpark streaming for 80 steps against the same config before this commit: fwd+bwd 374.7 ms vs 373.3 / 376.7, mean loss over steps 21-45 2.6541 vs 2.6543 / 2.6545, and the loss spikes this config shows at steps 46-55 land on the same steps within the controls' range. Rebased over #2589, which let LiLiCorr train offline by rebuilding full-sequence target logits whenever _needs_base_logits says a term reads them. Here the target is read per row instead: _candidate_target_logits, which #2589 shares between the distractor penalty and its new calibration term, projects just the candidates through _teacher_logits; offline, _needs_base_logits now only makes the entry fail early when the batch carries neither logits nor the final hidden; and #2589's cast of a wider-dtype captured hidden to lm_head's dtype moves into _teacher_logits, still ahead of the final norm. #2670 dropped the Qwen3-VL position-id workaround, so the multimodal branch passes position_ids through as main does. Signed-off-by: h-guo18 <67671475+h-guo18@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: 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'stf_minleg moves totransformers~=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 failedhf_ptqCI 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.tomlhfextra, the "not tested" warning inmodelopt/torch/__init__.py, andnoxfile.pytf_min.tf_mindrops itsdiffusers<0.40anddatasets<5.1(Pin datasets<5.1 in the tf_min unit-test env #2666) pins. Both only worked around transformers 4.57'shuggingface_hub<1.0cap, and 5.5 allowshuggingface_hub>=1.0.Removed: dead version branches. These include:
TRANSFORMERS_VERSION_GE_5_0(quantization plugin) and_TRANSFORMERS_GE_5_0(opt plugin)._QuantSparseSequentialMoe, the zero3 loader patch, the tied-weights shim, and the expert-removal hook.transformers<5.3CVE warning.Removed: dead workarounds. On every supported version, these either never ran or did nothing:
_QuantQwen3VLMoeTextExperts. 5.5 already ships the fusedQwen3VLMoeTextExpertslayout, so it was never registered._undo_torch_init_override_by_transformers. 5.x movedTORCH_INIT_FUNCTIONSout ofmodeling_utils, so this was a no-op from 5.0 on.attention_typecopies. 5.5+ decoder layers have noattention_type.convert_file_size_to_intimport attempts (removed in 5.x).Unguarded imports: these now exist in every supported version:
Llama4TextExperts,FalconLinear,FP8LinearandGptOssExpertsin the quantization plugin.conversion_mappingandcore_model_loadinginmodel_load_utils.Updated:
min_transformers_versionto the new 5.5 floor (DeepSeek-V4 keeps 5.8), asmodelopt/torch/models/README.mdprescribes.from_pretrained/from_configcalls usedtype=instead of thetorch_dtype=that 5.x deprecates.examples/puzzletron/requirements.txtdropstransformers<5.0, which would otherwise downgrade below the new floor.warmup_ratiocomments in the gpt-oss/llm_qat configs and notebooks are gone.Tests:
TestQuantSparseSequentialMoenow runs on a synthetic sequential MoE block instead of 4.x's tiny Qwen3-MoE, so it runs again.test_peft_flowgets a looser tolerance (atol1e-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
_QuantSparseSequentialMoetoken forcing assertinggate.top_k, which broke remote-code blocks that keeptop_kon the block or name the countn_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-endmtq.quantizeregressions cover it.Not changed:
_T5QuantAttentionstays: T5 moved to the attention interface only in 5.15._hf_tp_planTP path stays: 5.5–5.15 still use it.warmup_ratio→warmup_stepstranslation stays: 5.15 removedwarmup_ratio._QuantSparseSequentialMoeandregister_sparse_moe_on_the_flystay: remote-code MoEs still use per-expertnn.Linear._checkpoint_conversion_mappingrenames stay for remote code.rope_thetastays in the EAGLE default config: the Megatron EAGLE plugin reads it.Usage
Testing
All on this host (Python 3.13, torch 2.14, 2x RTX 6000 Ada), on this branch.
Qwen3VLMoeTextExpertsalready has the fused layout on 5.5.4.modeling_utils.TORCH_INIT_FUNCTIONSandtransformers.utils.convert_file_size_to_intare gone. 5.5.4 still haswarmup_ratio, the_hf_tp_planTP path and pre-interface T5.nox -s "unit-3.13(torch_214, tf_min)"(fresh env: transformers 5.5.4,datasets5.1.0,huggingface_hub1.33.0): 4506 passed, 38 skipped, 0 failed.test_transformers_tp.py(2 GPUs) andtest_vllm_fakequant_hf_export.py: 6 passed.tests/uniton 5.8.1 (fresh nox env): 4502 passed, 37 skipped, 0 failed.tests/uniton 5.18.0: 4530 passed, 9 skipped, 0 failed.tests/unit/torch/quantizationafter 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.test_transformers_tp.py(2 GPUs) andtest_vllm_fakequant_hf_export.pypass (6 passed).tests/gpu/torch/puzzletron/test_puzzletron.pyon 5.18.0: 7 passed, 2 skipped (nomamba_ssm). This covers the removed Qwen2/Qwen3/GPT-OSS dummy-block overrides.lm_losscheck, and main's code gives the same values. The expected losses are tuned for 5.18.test_nemotron_h_gpu_validation.py(nomamba-ssmhere) and the example suites.Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
Users still pinning transformers 4.x in this repo, left as is (owners to decide):
OVERRIDE_TRANSFORMERS=4.57.x,fsdp2_buffer_patch.py,tools/launcher/examples/MiniMax/MiniMax-M2.7-DFlash).mbridge_prune.yaml(transformers<5onnemo:26.04).examples/windows/*(ONNX flows,transformers<5.0/==4.57.3).experimental/dms(standalone, pins 4.57.3).uv.lockis left to the weekly relock job.🤖 Generated with Claude Code
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