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https://github.com/NVIDIA/Model-Optimizer.git
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Chenjie LuoandClaude Opus 4.8
56c4af2333
### What does this PR do?
Type of change: new feature (recipes)
Several `general/ptq` recipe families shipped a data-driven FP8 KV-cache
(`-kv_fp8`) variant but lacked the constant-amax `kv_fp8_cast` companion
that `fp8_default` and `nvfp4_default` already have. This PR adds the
missing cast variants so every KV-quantizing (and the weight-only)
family offers the calibration-free FP8 KV-cache option:
- `general/ptq/nvfp4_experts_only-kv_fp8_cast`
- `general/ptq/nvfp4_mlp_only-kv_fp8_cast`
- `general/ptq/nvfp4_omlp_only-kv_fp8_cast`
- `general/ptq/nvfp4_weight_only-kv_fp8_cast`
Each new recipe composes the exact same model-quant config as its
existing sibling and swaps the `kv_fp8` unit for the shared
`kv_fp8_cast` unit (constant-amax FP8 KV cache; no KV calibration
forward pass). The docs guide table/tree and the changelog are updated
to match.
### Usage
```bash
python examples/llm_ptq/hf_ptq.py \
--pyt_ckpt_path <model> \
--recipe general/ptq/nvfp4_mlp_only-kv_fp8_cast
```
### Testing
Extended the built-in PTQ smoke test
`tests/unit/recipe/test_loader.py::test_load_recipe_all_builtins` with
the four new recipe paths; all four load into a valid
`ModelOptPTQRecipe` with a populated `quantize` section.
```
$ python -m pytest tests/unit/recipe/test_loader.py tests/unit/recipe/test_presets.py -q
180 passed
```
`pre-commit` (including the `validate modelopt recipes` hook) passes on
all changed files.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ (additive — only new recipe
files)
- 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?: ✅ (extended the builtin recipe
smoke test)
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅
- Did you get Claude approval on this PR?: ❌ (not yet)
### Additional Information
The two weight-only families were discussed for scope;
`nvfp4_weight_only` is included (it already names a KV mode, `kv_fp16`),
while `int4_blockwise_weight_only` is intentionally left untouched since
it carries no `-kv_` composition.
🤖 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 four new NVFP4 PTQ (Post-Training Quantization) recipe variants:
experts-only, MLP-only, OMLP-only, and weight-only configurations.
* All new recipes include FP8 KV-cache cast mode support for improved
inference performance.
* **Documentation**
* Updated built-in recipes guide with new NVFP4 recipe options and
repository layout.
* **Tests**
* Expanded recipe loader test coverage for new recipe configurations.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>