# chipformer

**Repository Path**: kiloGrand/chipformer

## Basic Information

- **Project Name**: chipformer
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-29
- **Last Updated**: 2025-03-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## ChiPFormer: Transferable Chip Placement via Offline Decision Transformer

An offline RL placement method ChiPFormer, significantly improving the design quality and efficiency.

###  Publication
Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, Ping Luo. "ChiPFormer: Transferable Chip Placement via Offline Decision Transformer." International Conference on Machine Learning, ICML (2023): 18346-18364.

[paper](https://arxiv.org/pdf/2306.14744.pdf) | [dataset](https://drive.google.com/drive/folders/1F7075SvjccYk97i2UWhahN_9krBvDCmr) | [website](https://sites.google.com/view/chipformer/home) | [video(English)](https://www.youtube.com/watch?v=9-EQmDjRLHQ) | [video(Mandarin)](https://www.bilibili.com/video/BV1ym4y177CC/)


### Usage

#### Download the offline placement dataset

Download the offline placement dataset from [Google Drive](https://drive.google.com/drive/folders/1F7075SvjccYk97i2UWhahN_9krBvDCmr). (We provide the placement data including 12 benchmarks: adaptec1-4, bigblue1-4, ibm01-04, but you can just download a single benchmark file for training.) (24/03/02 Updated)

For a quick start, you can directly unzip the *adaptec1_small.pkl* data in this repo.

```
tar -zxvf adaptec1_small.pkl.tar.gz
```

#### Download placement benchmark files

Our test dataset includes ISPD05 and ICCAD04 datasets. We provide the *adaptec1* dataset for a quick start. For all benchmarks, they can also be downloaded from the *placement_bench.zip* file in [Google Drive](https://drive.google.com/drive/folders/1F7075SvjccYk97i2UWhahN_9krBvDCmr).

#### Quick start

Our ChiPFormer includes the pretraining and finetuning two parts.

- For pretraining:

```
python run_dt_place.py
```

The dataset file for training can be modified in *create_dataset.py*. 
The saved models are in the folder *save_models*.

- For finetuning:

```
python odt.py --benchmark=adaptec1
```
The model path for funetuning can be modified in *odt.py*. Typically, the optimal model files obtained from pre-training are used to fine-tune.


#### VGAE circuit feature extracture

(2024/05/25 updated)

For using VAGE, you need to firstly train the model, and use this model to evaluate the given benchmarks.

```
python graph_train.py
```

Then the trained model would be saved in the directory ```save_graph_models/```.

```
python graph_eval.py
```

The circuit embeddings are saved in the file ```circuit_g_token*.pkl```.

Then, you should modify the file ```mingpt/place_db.py``` for the line

```
def __init__(self, benchmark = None, offset = 0, is_graph = False):
```
to 
```
def __init__(self, benchmark = None, offset = 0, is_graph = True):
```

We also provide the example model and embedding file which you can directly use.

### Parameter

For *run_dt_place.py*:

- **seed** Random seed.
- **context_length** Maximum length of decision transformer.
- **epochs** Maximum training epochs.
- **batch_size** Batch size.
- **cuda** GPU label for use.
- **is_eval_only** Whether to evaluate function. (In evaluation function, it will place all macros rather than the maximum length number of macros)
- **test_all_macro** Whether to place all existing macros.

For *odt.py*:
- **replay_size** Replay buffer size for finetuning.
- **traj_len** Maximum length of decision transformer.
- **batch_size** Batch size.
- **benchmark** Circuit benchmark for finetuning.
- **max_online_iters** Maximum number of iterations for finetuning.
- **eval_interval** Evaluation every how many iterations.
- **exploration_rtg** Return-to-go value for exploration.
- **is_fifo** Whether to use fifo buffer or priority queue buffer. 
- **cuda** GPU label for use.

### Dependency
- [Python](https://www.python.org/) >= 3.9

- [Pytorch](https://pytorch.org/) >= 1.10

  - Other versions may also work, but not tested

- [tqdm](https://tqdm.github.io/)


### Reference code
The code refers to the following open source repos:
- [decision-transformer](https://github.com/kzl/decision-transformer)
- [online-dt](https://github.com/facebookresearch/online-dt)

### Citation
If you find our paper/code useful in your research, please cite

```
@inproceedings{lai2023chipformer,
  author       = {Lai, Yao and Liu, Jinxin and Tang, Zhentao and Wang, Bin and Hao, Jianye and Luo, Ping},
  title        = {ChiPFormer: Transferable Chip Placement via Offline Decision Transformer},
  booktitle    = {International Conference on Machine Learning, {ICML} 2023, 23-29 July
                  2023, Honolulu, Hawaii, {USA}},
  series       = {Proceedings of Machine Learning Research},
  volume       = {202},
  pages        = {18346--18364},
  publisher    = {{PMLR}},
  year         = {2023},
  url          = {https://proceedings.mlr.press/v202/lai23c.html},
}
```