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pull-request/2240
3
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Keval MorabiaandClaude Sonnet 4.6
|
80a77d1cc0 |
Add LTX-2 third-party license notices for legal compliance (#1226)
## Summary LTX-2 (`ltx-core`, `ltx-pipelines`, `ltx-trainer`) is a third-party dependency developed and provided by Lightricks. It is governed by the [LTX Community License Agreement](https://github.com/Lightricks/LTX-2/blob/main/LICENSE), **not** the Apache 2.0 license that covers NVIDIA Model Optimizer. Per legal guidance, all integration points must clearly surface this to users. - Add `[!WARNING]` license notice blocks at the top of all LTX-2-related READMEs (`examples/diffusers`, `examples/diffusers/distillation`, `examples/windows/diffusers/qad_example`) - Add `warnings.warn(UserWarning)` at every LTX package import site in Python files, covering both top-level and lazy imports: - `examples/diffusers/distillation/distillation_trainer.py` - `examples/diffusers/quantization/calibration.py` - `examples/diffusers/quantization/pipeline_manager.py` - `examples/windows/diffusers/qad_example/sample_example_qad_diffusers.py` - `modelopt/torch/export/diffusers_utils.py` - `modelopt/torch/quantization/plugins/diffusion/ltx2.py` - Add license notice comment to `requirements.txt` files that list LTX packages, so the obligation is visible at install time - Update `.github/CODEOWNERS` so all `requirements*.txt` files (covering variants like `requirements-dev.txt`) are owned by `@NVIDIA/modelopt-setup-codeowners` regardless of location, via a last-match-wins rule **Design notes:** - For library files (`diffusers_utils.py`, `ltx2.py`), the warning is placed at the lazy import site inside functions — it fires only when LTX-2 code paths are actually invoked, not at module import time, to avoid polluting non-LTX users - For example entry-point scripts that are LTX-2-only, the warning fires at module load time (after all imports, to satisfy ruff E402) ## Test plan - [ ] Confirm `pre-commit run --all-files` passes (ruff, mypy, markdownlint, bandit all clean) - [ ] Verify warning appears at runtime when running an LTX-2 quantization or distillation example - [ ] Confirm non-LTX code paths (FLUX, SDXL, SD3) do not emit the warning 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Documentation** * Added third-party license notices across documentation and requirements files clarifying LTX-2 packages are governed by the LTX Community License Agreement rather than NVIDIA Model Optimizer's Apache 2.0 license. * **Chores** * Updated code ownership configuration for requirements files. * Added runtime warnings to notify when LTX-2 dependencies are accessed. <!-- 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 Sonnet 4.6 <noreply@anthropic.com> |
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|
Keval Morabia
|
04cd596d79 |
Add experimental support for transformers>=5.0 + min torch 2.8 (#975)
### What does this PR do? - Add experimental support for transformers >=5.0 and remove deprecated usages: https://github.com/huggingface/transformers/blob/main/MIGRATION_GUIDE_V5.md - ⚠️ For accelerate examples that used `--warmup-ratio: float` (deprecated in 5.x), we now change it to `--warmup-steps: float | int` which works as ratio if float but only for 5.x. For 4.x, it will error out if float and prompt user to change back to `--warmup-ratio` or pass an int absolute step count. - ⚠️ Unified Hugging Face checkpoint export for quantized checkpoints may not work for some models with transformers>=5.0 yet as it requires a lot of fixes (e.g. change in how MoE experts are organized) - ~Add Workaround for TRT-LLM's import of deprecated transformers functions so trt-llm based gpu unit tests work fine. Still deployment for models needs proper fixes directly in TRT-LLM hence llm/vlm ptq example tests still run with transformers 4.57~ - Everything except PTQ and Export (mainly MoE) should work fine with transformers>=5.0 - Bump min torch to 2.8 and enable 2.11 cicd testing - NOTE: Upcoming Nemo:26.04 container comes with transformers 5.3 ### Testing <!-- Mention how have you tested your change if applicable. --> - [x] CI/CD tests passing - [x] Manually tested unit tests, gpu tests with transformers 4.56 and 5.4 - [x] Manually tested example tests (except trt-llm container tests) with transformers 4.56 and 5.4 - [x] 2-gpu nightly CICD tests manually triggered and passing: [gpu tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867257540), [example tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867260643) ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, using `torch.load(..., weights_only=True)`, avoiding `pickle`, etc.). - Is this change backward compatible?: ✅ <!--- If ❌, explain why. --> - If you copied code from any other source, did you follow IP policy in [CONTRIBUTING.md](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md#-copying-code-from-other-sources)?: N/A <!--- Mandatory --> - Did you write any new necessary tests?: ✅ <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Make remote-code usage opt-in via a configurable --trust_remote_code flag across examples and tools. * **Bug Fixes** * Improve checkpoint/resume detection and related training guidance to avoid erroneous errors. * **Refactor** * Consolidate dtype/config naming, switch warmup settings from ratio → steps, and unify tokenizer invocation patterns. * **Documentation** * Simplify changelog title and add misc notes for release 0.44. * **Chores** * Remove scheduled PR-branch cleanup workflow and relax/remove several transformers version pins. * **Tests** * Adjust test gates, skips, and structures to align with updated deps and behaviors. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> |
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|
ynankani-nv
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4a70040ddd |
Ynankani/fvd benchmark (#1182)
### What does this PR do? Type of change: ? New benchmark addition <!-- Details about the change. --> This PR adds the following to examples/windows/: 1) FVD (Fréchet Video Distance) evaluation tool — a standalone script for computing FVD between two sets of videos using a pre-trained I3D model (Kinetics-400, 1024-dim pooled features). 2) Directory reorganization — moved torch_onnx/ and qad_example/ under a new diffusers/ folder for clearer grouping of diffusion model examples. 3) Minor fixes: Updated WikiText-2 dataset link in perplexity_metrics/README.md (old wikitext → Salesforce/wikitext) ### Usage ```python python compute_fvd.py \ --ref-dir /path/to/reference/videos \ --gen-dir /path/to/generated/videos \ --output results.json \ --device cuda ``` ### Testing Evaluated FVD tool on LTX-2.3 outputs comparing QAD vs PTQ checkpoints (both NVFp4) against a BF16 baseline across 11 VBench dimensions. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A <!--- Mandatory --> - Did you write any new necessary tests?: N/A <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added FVD (Fréchet Video Distance) evaluation: command‑line tool, I3D‑based feature extraction, PCA option, and quantized comparisons (PTQ vs QAD) with per‑category and overall results showing QAD's lower average FVD. * **Documentation** * Expanded benchmarks TOC with “Additional Metrics”, added FVD evaluation guide, usage examples, troubleshooting, and updated perplexity dataset reference. * **Chores** * Added Python requirements for the FVD benchmark. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: ynankani <ynankani@nvidia.com> Signed-off-by: Yash Nankani <ynankani@nvidia.com> |