Metadata-Version: 2.4
Name: campd
Version: 0.1.1
Summary: Framework for training context-aware diffusion models on trajectory data
Author-email: Edward Sandra <edward.sandra@kuleuven.be>, Matthias Van EysenDeyk <matthias.vaneysendeyk@kuleuven.be>
License:      GNU LESSER GENERAL PUBLIC LICENSE
                               Version 3, 29 June 2007
        
         Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
         Everyone is permitted to copy and distribute verbatim copies
         of this license document, but changing it is not allowed.
        
        
          This version of the GNU Lesser General Public License incorporates
        the terms and conditions of version 3 of the GNU General Public
        License, supplemented by the additional permissions listed below.
        
          0. Additional Definitions.
        
          As used herein, "this License" refers to version 3 of the GNU Lesser
        General Public License, and the "GNU GPL" refers to version 3 of the GNU
        General Public License.
        
          "The Library" refers to a covered work governed by this License,
        other than an Application or a Combined Work as defined below.
        
          An "Application" is any work that makes use of an interface provided
        by the Library, but which is not otherwise based on the Library.
        Defining a subclass of a class defined by the Library is deemed a mode
        of using an interface provided by the Library.
        
          A "Combined Work" is a work produced by combining or linking an
        Application with the Library.  The particular version of the Library
        with which the Combined Work was made is also called the "Linked
        Version".
        
          The "Minimal Corresponding Source" for a Combined Work means the
        Corresponding Source for the Combined Work, excluding any source code
        for portions of the Combined Work that, considered in isolation, are
        based on the Application, and not on the Linked Version.
        
          The "Corresponding Application Code" for a Combined Work means the
        object code and/or source code for the Application, including any data
        and utility programs needed for reproducing the Combined Work from the
        Application, but excluding the System Libraries of the Combined Work.
        
          1. Exception to Section 3 of the GNU GPL.
        
          You may convey a covered work under sections 3 and 4 of this License
        without being bound by section 3 of the GNU GPL.
        
          2. Conveying Modified Versions.
        
          If you modify a copy of the Library, and, in your modifications, a
        facility refers to a function or data to be supplied by an Application
        that uses the facility (other than as an argument passed when the
        facility is invoked), then you may convey a copy of the modified
        version:
        
           a) under this License, provided that you make a good faith effort to
           ensure that, in the event an Application does not supply the
           function or data, the facility still operates, and performs
           whatever part of its purpose remains meaningful, or
        
           b) under the GNU GPL, with none of the additional permissions of
           this License applicable to that copy.
        
          3. Object Code Incorporating Material from Library Header Files.
        
          The object code form of an Application may incorporate material from
        a header file that is part of the Library.  You may convey such object
        code under terms of your choice, provided that, if the incorporated
        material is not limited to numerical parameters, data structure
        layouts and accessors, or small macros, inline functions and templates
        (ten or fewer lines in length), you do both of the following:
        
           a) Give prominent notice with each copy of the object code that the
           Library is used in it and that the Library and its use are
           covered by this License.
        
           b) Accompany the object code with a copy of the GNU GPL and this license
           document.
        
          4. Combined Works.
        
          You may convey a Combined Work under terms of your choice that,
        taken together, effectively do not restrict modification of the
        portions of the Library contained in the Combined Work and reverse
        engineering for debugging such modifications, if you also do each of
        the following:
        
           a) Give prominent notice with each copy of the Combined Work that
           the Library is used in it and that the Library and its use are
           covered by this License.
        
           b) Accompany the Combined Work with a copy of the GNU GPL and this license
           document.
        
           c) For a Combined Work that displays copyright notices during
           execution, include the copyright notice for the Library among
           these notices, as well as a reference directing the user to the
           copies of the GNU GPL and this license document.
        
           d) Do one of the following:
        
               0) Convey the Minimal Corresponding Source under the terms of this
               License, and the Corresponding Application Code in a form
               suitable for, and under terms that permit, the user to
               recombine or relink the Application with a modified version of
               the Linked Version to produce a modified Combined Work, in the
               manner specified by section 6 of the GNU GPL for conveying
               Corresponding Source.
        
               1) Use a suitable shared library mechanism for linking with the
               Library.  A suitable mechanism is one that (a) uses at run time
               a copy of the Library already present on the user's computer
               system, and (b) will operate properly with a modified version
               of the Library that is interface-compatible with the Linked
               Version.
        
           e) Provide Installation Information, but only if you would otherwise
           be required to provide such information under section 6 of the
           GNU GPL, and only to the extent that such information is
           necessary to install and execute a modified version of the
           Combined Work produced by recombining or relinking the
           Application with a modified version of the Linked Version. (If
           you use option 4d0, the Installation Information must accompany
           the Minimal Corresponding Source and Corresponding Application
           Code. If you use option 4d1, you must provide the Installation
           Information in the manner specified by section 6 of the GNU GPL
           for conveying Corresponding Source.)
        
          5. Combined Libraries.
        
          You may place library facilities that are a work based on the
        Library side by side in a single library together with other library
        facilities that are not Applications and are not covered by this
        License, and convey such a combined library under terms of your
        choice, if you do both of the following:
        
           a) Accompany the combined library with a copy of the same work based
           on the Library, uncombined with any other library facilities,
           conveyed under the terms of this License.
        
           b) Give prominent notice with the combined library that part of it
           is a work based on the Library, and explaining where to find the
           accompanying uncombined form of the same work.
        
          6. Revised Versions of the GNU Lesser General Public License.
        
          The Free Software Foundation may publish revised and/or new versions
        of the GNU Lesser General Public License from time to time. Such new
        versions will be similar in spirit to the present version, but may
        differ in detail to address new problems or concerns.
        
          Each version is given a distinguishing version number. If the
        Library as you received it specifies that a certain numbered version
        of the GNU Lesser General Public License "or any later version"
        applies to it, you have the option of following the terms and
        conditions either of that published version or of any later version
        published by the Free Software Foundation. If the Library as you
        received it does not specify a version number of the GNU Lesser
        General Public License, you may choose any version of the GNU Lesser
        General Public License ever published by the Free Software Foundation.
        
          If the Library as you received it specifies that a proxy can decide
        whether future versions of the GNU Lesser General Public License shall
        apply, that proxy's public statement of acceptance of any version is
        permanent authorization for you to choose that version for the
        Library.
        
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: einops==0.8.1
Requires-Dist: h5py
Requires-Dist: pyyaml
Requires-Dist: matplotlib
Requires-Dist: diffusers
Requires-Dist: accelerate
Requires-Dist: torchjd
Requires-Dist: pydantic
Requires-Dist: pydantic_numpy
Requires-Dist: wandb
Requires-Dist: experiment-launcher-meco
Provides-Extra: docs
Requires-Dist: sphinx>=7.0; extra == "docs"
Requires-Dist: furo; extra == "docs"
Requires-Dist: sphinx-autodoc-typehints; extra == "docs"
Requires-Dist: sphinx-copybutton; extra == "docs"
Requires-Dist: myst-parser; extra == "docs"
Dynamic: license-file

# Context-Aware Motion Planning Diffusion


A modular framework for training, and running inference with context-aware diffusion models on trajectory data. It provides a registry-driven component system, YAML-based configuration, and built-in support for HuggingFace Accelerate (multi-GPU), CUDA graphs, Weights & Biases logging, and experiment sweeps.

This framework accompanies the paper:

> **Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Models**
> Edward Sandra, Lander Vanroye, Dries Dirckx, Ruben Cartuyvels, Jan Swevers, Wilm Decré
> arXiv:2510.14615 — [https://arxiv.org/abs/2510.14615](https://arxiv.org/abs/2510.14615)

If you use this framework in your research, please cite:

```bibtex
@misc{sandra2025campd,
  title   = {Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Models},
  author  = {Sandra, Edward and Vanroye, Lander and Dirckx, Dries and Cartuyvels, Ruben and Swevers, Jan and Decr\'{e}, Wilm},
  year    = {2025},
  eprint  = {2510.14615},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url     = {https://arxiv.org/abs/2510.14615},
}
```

---

## API Documentation

Full API documentation for the project's codebase, including all registries, architectures, and experiments, is available at the [CAMPD API Docs](https://meco-group.github.io/campd/api/).

---

## Table of Contents

- [Overview](#overview)
- [Installation](#installation)
- [Training Data](#training-data)
- [Core Concepts](#core-concepts)
  - [Registry System](#registry-system)
  - [The `Spec` Pattern](#the-spec-pattern)
  - [Configuration & Attribute Propagation](#configuration--attribute-propagation)
  - [Dependencies / Imports in YAML](#dependencies--imports-in-yaml)
- [Launching Experiments](#launching-experiments)
  - [Via `campd-run` (recommended)](#via-campd-run-recommended)
  - [Via custom Python script](#via-custom-python-script)
- [YAML Configuration Reference](#yaml-configuration-reference)
- [Extending the Framework](#extending-the-framework)
- [Built-in Components](#built-in-components)
- [Troubleshooting](#troubleshooting)

---

## Overview

CAMPD is built around a **diffusion-model pipeline for trajectory generation** conditioned on (but not limited to) environment context (e.g. obstacle geometries). The high-level flow is:

1. **Data** — Load trajectory datasets from HDF5 files (with context fields like cuboid/cylinder/sphere obstacle descriptions).
2. **Model** — A `ContextTrajectoryDiffusionModel` wrapping HuggingFace Diffusers schedulers, a reverse-diffusion denoising network (e.g. `TemporalUnet`), and an optional context encoder.
3. **Training** — A `Trainer` runs the training loop with configurable objectives, callbacks, summaries, multi-objective optimization (TorchJD), AMP, gradient clipping, and optional CUDA graph acceleration.
4. **Inference** — Load a trained checkpoint and sample trajectories, optionally validating them with domain-specific validators.

Everything is wired together through **YAML config files** and a **registry system**, so you can swap components without changing code.

---

## Installation

### Prerequisites

- Python ≥ 3.10
- CUDA-capable GPU (recommended)

### Steps

```bash
pip install campd
```

This installs the `campd` package and the `campd-run` CLI entry point. 

> **Note:** Some example projects (e.g. `examples/franka_curobo/`) may have additional dependencies not listed in `pyproject.toml` (e.g. `curobo`, `pinocchio`). These are installed separately; check each example's `requirements.txt`.

> **Note:** If you want to enable Weights and Biases logging, install it with `pip install wandb` and run `wandb login` to authenticate.

---

## Training Data

Training datasets for the **sphere-based** and **MPINets** environments are stored as [Git LFS](https://git-lfs.github.com) objects in a separate repository. Download and extract them before running the example experiments:

```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/meco-group/campd-data.git /tmp/campd-data
(cd /tmp/campd-data && git lfs pull)
mkdir -p data/train/
tar -xzf /tmp/campd-data/train_data_campd_franka_spheres.tar.gz -C data/train/
tar -xzf /tmp/campd-data/train_data_campd_mpinets.tar.gz        -C data/train/
rm -rf /tmp/campd-data
```

---

## Core Concepts

### Registry System

The framework uses a **registry pattern** to enable config-driven component selection. Each category of component has its own `Registry` instance:

| Registry           | Module                                | Purpose                           |
|:-------------------|:--------------------------------------|:----------------------------------|
| `EXPERIMENTS`      | `experiments/registry.py`             | Experiment types                  |
| `MODULES`          | `architectures/registry.py`           | Generic `nn.Module` building blocks |
| `REVERSE_NETS`     | `architectures/registry.py`           | Denoising networks                |
| `CONTEXT_NETS`     | `architectures/registry.py`           | Context encoder networks          |
| `LOSSES`           | `training/registry.py`               | Loss functions                    |
| `CALLBACKS`        | `training/registry.py`               | Training callbacks                |
| `SUMMARIES`        | `training/registry.py`               | Training summaries                |
| `OBJECTIVES`       | `training/registry.py`               | Training objectives               |
| `VALIDATORS`       | `experiments/validators.py`           | Inference validators              |

Components self-register using a decorator:

```python
from campd.training.registry import CALLBACKS

@CALLBACKS.register("MyCallback")
class MyCallback(Callback):
    ...
```

Then in the YAML config:

```yaml
callbacks:
  - cls: "MyCallback"
```

**Critical:** For a component to be available at runtime, its module must be **imported** before the registry lookup happens. This is handled by two mechanisms:

1. `campd/all_imports.py` — Bulk-imports all built-in subpackages (architectures, data, experiments, models, training), which triggers their `__init__.py` chains and populates the registries with built-in components.
2. The `dependencies` key in YAML config — Imports external/example-specific modules at startup (see [Dependencies / Imports in YAML](#dependencies--imports-in-yaml)).

### The `Spec` Pattern

`Spec` (defined in `utils/registry.py`) is a Pydantic model that describes **how to build an object** from config. It supports two modes:

#### Init Mode — Direct constructor kwargs

```yaml
optimizer:
  cls: "torch.optim.Adam"      # Full import path or registry key
  init:
    lr: 1.0e-4
    weight_decay: 0.0
```

This calls `torch.optim.Adam(lr=1e-4, weight_decay=0.0)`.

#### Config Mode — Factory method via `from_config`

```yaml
objective:
  cls: "DiffusionObjective"    # Registry key
  config:
    loss_fn:
      cls: "torch.nn.MSELoss"
      init:
        reduction: "mean"
```

This calls `DiffusionObjective.from_config(config_dict)`. The class must have a `from_config` classmethod.

#### Registry vs Import Path Resolution

- If a `registry` field is set on the `Spec`, the `cls` string is looked up in that specific registry.
- Otherwise, the `cls` string is first tried as a **registry key** (if a registry is passed to `build_from`), then as a **Python import path** (e.g. `torch.optim.Adam`).
- This means you can reference any importable class by its full dotted path, or use short registry keys for registered components.

### Configuration & Attribute Propagation

The framework uses **Pydantic models** for configuration validation. A key feature is **attribute propagation**: parent-level config values are automatically pushed down to nested child configs that share the same field name. For example:

```yaml
experiment:
  device: "cuda:0"           # Parent-level
  dataset:
    # device is NOT declared here, but if TrajectoryDatasetCfg has a
    # 'device' field, it will receive "cuda:0" from the parent.
    ...
  trainer:
    tensor_args:
      device: "cuda:0"       # Explicit — but could also be propagated
```

YAML anchors (`&name` / `*name`) can be used in config files for DRY configuration.

### Dependencies / Imports in YAML

The `dependencies` top-level key in YAML configs lists modules or directories that should be imported before the experiment runs. This is essential for **registering custom components** (e.g. example-specific summaries, validators, architectures):

```yaml
dependencies:
  - "../src"              # A directory — all .py files inside are imported
  - "my_custom_module"    # A Python module import path
  - "./my_file.py"        # A single Python file
```

Paths are resolved relative to the config file's directory.

**This is the mechanism that makes custom components available to the registries.** If you define a custom `@SUMMARIES.register("ValidationSummary")` class in `examples/franka_curobo/src/training_summary.py`, listing `"../src"` in `dependencies` ensures it's imported and registered before the config tries to reference `"ValidationSummary"`.

---

## Launching Experiments

### Via `campd-run` (recommended)

The `campd-run` CLI is installed as a console script by pip:

```bash
campd-run path/to/config.yaml
```

This:

1. Parses the YAML file.
2. Extracts `dependencies`, `experiment`, `wandb`, `launcher`, and `sweep` sections.
3. Imports built-in and user-defined dependencies to populate registries.
4. Uses [experiment-launcher](https://github.com/robot-learning-group/experiment-launcher) to manage experiment execution (seeding, output directories, optional SLURM submission).
5. Looks up the experiment class via `experiment.cls` in the `EXPERIMENTS` registry and calls its `run()` method.

**Launcher configuration** controls experiment management:

```yaml
launcher:
  exp_name: "my_experiment"
  n_seeds: 1                  # Number of seeds (repetitions)
  start_seed: 0
  base_dir: "results/"        # Output base directory
  use_timestamp: true         # Append timestamp to output dir
  resources: 
    n_exps_in_parallel: 1       # Parallel experiments
    ... (see experiment-launcher docs)
```

### Via custom Python script

For simpler use cases or debugging, you can bypass the launcher:

```python
import os
from campd.experiments import TrainExperiment

base_dir = os.path.dirname(os.path.abspath(__file__))
exp = TrainExperiment.from_yaml(os.path.join(base_dir, "configs/train.yaml"))
exp.run()
```

> **Note:** When using `from_yaml`, the `dependencies` section is **not** processed automatically. You must import your custom modules manually before calling `from_yaml` (e.g. `import my_custom_module`).
>
> The `campd-run` CLI handles this for you.

---

## YAML Configuration Reference

A full config file has up to five top-level sections:

```yaml
# 1. Dependencies — modules/directories to import for custom registrations
dependencies:
  - "../src"

# 2. WandB — Weights & Biases logging
wandb:
  mode: "online"           # "online", "offline", or "disabled"
  entity: "my-team"
  project: "my-project"
  group: "group_name"
  name: &name "run_name"
  

# 3. Launcher — experiment-launcher settings
launcher:
  exp_name: *name
  base_dir: "results/"
  n_seeds: 1
  # ... (see experiment-launcher docs)

# 4. Sweep — hyperparameter sweep (optional)
sweep:
  trainer:
    lr: [1e-4, 1e-3]      # Creates one run per value

# 5. Experiment — the actual experiment configuration
experiment:
  cls: "train"             # Registered experiment key

  # Common fields (from ExperimentCfg):
  seed: 42
  device: "cuda:0"
  # results_dir: set by launcher

  # Experiment-specific fields (e.g. TrainExperimentCfg):
  dataset_dir: "data/train/my_dataset"
  train_file: "train.hdf5"
  val_file: "val.hdf5"    # Optional
  # val_set_size: 0.1       # Optional

  dataset:
    trajectory_state: "pos"          # "pos", "pos+vel", "pos+vel+acc"
    field_config:
      trajectory_field: "solutions"  # HDF5 key for trajectory data
      q_dim: 7                       # Configuration-space dimension
      context_fields:                # Maps list of HDF5 keys -> context key
        cuboids: ["cuboid_centers", "cuboid_dims", "cuboid_quaternions"]
        # Note that the subkeys are still accessible inside the TrajectoryContext
        # object
      # also possible to use a list of HDF5 keys
      # context_fields:
      #   - "cuboid_centers"
      #   - "cuboid_dims"
      #   - "cuboid_quaternions"
    # ...

  model:
    state_dim: 7
    model_type: "epsilon"            # "epsilon", "sample", or "v_prediction"
    n_diffusion_steps: 25
    network:                         # Spec for reverse diffusion network
      cls: "TemporalUnet"
      config: { ... }
    context_network:                 # Spec for context encoder (optional)
      cls: "campd.architectures.context.encoder.ContextEncoder"
      config: { ... }

  trainer:
    max_epochs: 200
    optimizer:
      cls: "torch.optim.Adam"
      init: { lr: 1e-4 }
    objective:
      cls: "DiffusionObjective"
      config:
        loss_fn:
          cls: "torch.nn.MSELoss"
          init: { reduction: "mean" }
    callbacks:
      - cls: "PrinterCallback"
      - cls: "EMACallback"
        init: { decay: 0.995 }
      - cls: "CheckpointCallback"
        init: { save_best: true }
      - cls: "WandBCallback"
    summaries:
      - cls: "ValidationSummary"      # Custom (from dependencies)
        init: { every_n_steps: 2500 }
```

---

## Extending the Framework

The general pattern for adding a new component:

1. **Create a Python file** with your class, inheriting from the appropriate base class.
2. **Decorate** it with `@REGISTRY.register("key")` using the relevant registry.
3. **Make sure it's imported** at startup — either by placing it in a built-in subpackage (and re-exporting via `__init__.py`), or by listing its module/directory in the `dependencies` section of your YAML config.
4. **Reference it** in your YAML config via the registry key.

### Registering a New Experiment

```python
# my_experiments/custom_exp.py
from campd.experiments.base import BaseExperiment, ExperimentCfg
from campd.experiments.registry import EXPERIMENTS
from pydantic import validate_call

class MyExperimentCfg(ExperimentCfg):
    my_param: str = "default"

@EXPERIMENTS.register("my_experiment")
class MyExperiment(BaseExperiment):
    CfgClass = MyExperimentCfg

    @validate_call
    def __init__(self, cfg: MyExperimentCfg):
        super().__init__(cfg)

    def run(self):
        print(f"Running with param: {self.cfg.my_param}")
```

```yaml
dependencies:
  - "my_experiments"        # Directory containing custom_exp.py

experiment:
  cls: "my_experiment"
  my_param: "hello"
```

### Registering a New Network Architecture

```python
# my_networks/custom_net.py
import torch.nn as nn
from campd.architectures.registry import REVERSE_NETS
from campd.utils.registry import FromCfg

@REVERSE_NETS.register("MyDenoiser")
class MyDenoiser(nn.Module):
    def __init__(self, state_dim: int, hidden_dim: int):
        super().__init__()
        # ... build layers ...

    @classmethod
    def from_config(cls, cfg):
        if isinstance(cfg, dict):
            return cls(**cfg)
        return cls(**cfg.model_dump())

    def forward(self, x, t, context=None):
        # x: [B, T, state_dim], t: [B], context: EmbeddedContext or None
        ...
```

```yaml
model:
  network:
    cls: "MyDenoiser"
    config:
      state_dim: 7
      hidden_dim: 128
```

### Registering a New Training Callback

```python
from campd.training.callbacks import Callback
from campd.training.registry import CALLBACKS

@CALLBACKS.register("LRLoggerCallback")
class LRLoggerCallback(Callback):
    def on_epoch_end(self, trainer, train_losses=None):
        lr = trainer.optimizer.param_groups[0]['lr']
        print(f"Current LR: {lr}")
```

Available hooks: `on_train_start`, `on_fit_start`, `on_train_end`, `on_epoch_start`, `on_epoch_end`, `on_batch_start`, `on_batch_end`, `on_validation_start`, `on_validation_end`, `on_summary_end`.

### Registering a New Training Summary

```python
from campd.training.summary import Summary
from campd.training.registry import SUMMARIES

@SUMMARIES.register("MySummary")
class MySummary(Summary):
    def __init__(self, every_n_steps=1000):
        super().__init__(every_n_steps=every_n_steps)

    def _run(self, model, train_dataloader, val_dataloader, step):
        # Generate samples, compute metrics, return dict/figures
        return {"my_metric": 0.95}
```

### Registering a New Training Objective

```python
from campd.training.objectives.base import TrainingObjective
from campd.training.registry import OBJECTIVES

@OBJECTIVES.register("MyObjective")
class MyObjective(TrainingObjective):
    @classmethod
    def from_config(cls, cfg):
        return cls(cfg)

    def step(self, model, batch):
        # Return: (losses_dict, model_output, info_dict)
        loss = ...
        return {"my_loss": loss}, model_out, {}
```

### Registering a New Validator

```python
from campd.experiments.validators import Validator, VALIDATORS

@VALIDATORS.register("MyValidator")
class MyValidator(Validator):
    def validate(self, batch, output_dir):
        # Return dict of validation metrics
        return {"success_rate": 0.85}
```

---

## Built-in Components

### Experiments
| Key            | Class                      | Description                                |
|:---------------|:---------------------------|:-------------------------------------------|
| `"train"`      | `TrainExperiment`          | Full training pipeline (data → model → fit)|
| `"inference"`  | `InferenceExperiment`      | Load checkpoint & sample trajectories      |

### Callbacks
| Key                    | Description                                       |
|:-----------------------|:--------------------------------------------------|
| `"PrinterCallback"`   | Logs training start/end messages                  |
| `"EMACallback"`       | Exponential moving average of model weights       |
| `"CheckpointCallback"`| Saves checkpoints (best, last, periodic)          |
| `"WandBCallback"`     | Logs metrics/artifacts to Weights & Biases        |
| `"EarlyStoppingCallback"` | Stops training when validation loss plateaus  |

### Objectives
| Key                      | Description                                  |
|:-------------------------|:---------------------------------------------|
| `"DiffusionObjective"`   | Standard diffusion loss (ε, sample, or v)    |

### Losses
| Key            | Description      |
|:---------------|:-----------------|
| `"WeightedL1"` | Weighted L1 loss |
| `"WeightedL2"` | Weighted L2 loss |
| `"MSE"`        | `nn.MSELoss`     |
| `"L1"`         | `nn.L1Loss`      |

---

## Troubleshooting

- **CUDA graph errors**: If `use_cuda_graph: true` and you get runtime errors, ensure your PyTorch CUDA version matches the system CUDA version. Also verify that all tensor shapes remain constant across batches (CUDA graphs require fixed shapes).

- **`KeyError: Unknown 'X' in registry 'Y'`**: The component `X` is not registered. Ensure:
  1. The module defining the component is imported before the registry lookup.
  2. The module is listed in the `dependencies` section of your config.
  3. The `@REGISTRY.register("X")` decorator is present on the class.

If your issue isn't covered above, please [open a GitHub issue](../../issues) with a minimal reproducible example and the full error traceback.

## License

See [LICENSE](LICENSE) for details.
