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shengliangxu merged 1 commit into from
Mar 25, 2026
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[OMNIML-3779] Add recipe into CLAUDE.md #1106

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16 changes: 15 additions & 1 deletion CLAUDE.md
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Expand Up @@ -38,13 +38,14 @@ Primarily Python codebase with optional C++/CUDA extensions supporting PyTorch,

## Architecture

ModelOpt is organized into three top-level namespaces:
ModelOpt code base is organized into four top-level namespaces:

| Namespace | Path | Role |
|-----------|------|------|
| `modelopt.torch` | `modelopt/torch/` | Core PyTorch optimization library |
| `modelopt.onnx` | `modelopt/onnx/` | ONNX model quantization and export |
| `modelopt.deploy` | `modelopt/deploy/` | Deployment utilities for LLMs |
| `modelopt.recipe` | `modelopt/recipe/` | Recipe loading, parsing, and validation infrastructure |

### `modelopt.torch` Sub-packages

Expand All @@ -68,6 +69,15 @@ A **mode** is the unit of model optimization in ModelOpt. Each algorithm (quanti
etc.) is implemented as one or more modes. Modes are recorded in the model's `modelopt_state` so
optimization workflows can be composed, saved, and restored.

### Core Abstraction: Recipes

A **recipe** is a declarative YAML specification of an optimization configuration. Recipes decouple optimization specs from code, enabling reuse, sharing, and version control.

**Built-in recipes** (`modelopt_recipes/`):

- `general/ptq/` — general-purpose PTQ recipes
- `configs/` — shared configuration units referenced by recipes

## Key Files

| File | Role |
Expand All @@ -79,6 +89,9 @@ optimization workflows can be composed, saved, and restored.
| `modelopt/torch/export/unified_export_hf.py` | Unified HF checkpoint export |
| `modelopt/torch/export/model_config_export.py` | TRT-LLM model config export |
| `modelopt/deploy/llm/` | LLM deployment utilities |
| `modelopt/recipe/loader.py` | `load_recipe()` / `load_config()` public API |
| `modelopt/recipe/config.py` | Recipe Pydantic models (`ModelOptPTQRecipe`, `RecipeType`) |
| `modelopt_recipes/general/ptq/` | Built-in PTQ recipe YAML files |
| `pyproject.toml` | Optional dependency groups (`[onnx]`, `[hf]`, `[all]`, `[dev]`); ruff, mypy, pytest, bandit, and coverage config |
| `.pre-commit-config.yaml` | Pre-commit hooks (ruff, mypy, clang-format, license headers) |
| `tox.ini` | Test environment definitions |
Expand All @@ -92,6 +105,7 @@ optimization workflows can be composed, saved, and restored.
| **Optional dependencies** | Features gated by install extras (`[onnx]`, `[hf]`, `[all]`); avoid hard imports at module level |
| **Config dataclasses** | Each mode has a typed config; use Pydantic or dataclass conventions |
| **State dict** | Models carry `modelopt_state` for checkpoint save/restore across optimization steps |
| **Declarative recipes** | YAML-based optimization specs in `modelopt_recipes/`; loaded via `load_recipe()`, passed to the model optimization system |

## CI / Testing

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