pd: update paddle doc (#4489)

Add paddle icon svg and update related docs <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features**	- Added Paddle as a supported deep learning backend for DeePMD-kit. - Expanded documentation to include Paddle installation, training, and model support. - Updated documentation for various model descriptors and training methods to include Paddle. - **Documentation** - Added Paddle-specific installation instructions for different environments.	- Updated README and backend documentation to reflect Paddle support. - Expanded training, fine-tuning, and parallel training guides to include Paddle. - Introduced Paddle commands for model freezing and neighbor statistics. - Enhanced documentation with Paddle icons and details for various commands and configurations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: HydrogenSulfate <490868991@qq.com> Co-authored-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>

Author: HydrogenSulfate

README.md

@@ -19,7 +19,7 @@ For more information, check the [documentation](https://deepmd.readthedocs.io/).
 
 ### Highlighted features
 
-- **interfaced with multiple backends**, including TensorFlow, PyTorch, and JAX, the most popular deep learning frameworks, making the training process highly automatic and efficient.
+- **interfaced with multiple backends**, including TensorFlow, PyTorch, JAX, and Paddle, the most popular deep learning frameworks, making the training process highly automatic and efficient.
 - **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS.
 - **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc.
 - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing.

doc/_static/paddle.svg

@@ -5,7 +5,7 @@
 DeePMD-kit supports multiple backends: TensorFlow and PyTorch.
 To use DeePMD-kit, you must install at least one backend.
 Each backend does not support all features.
-In the documentation, TensorFlow {{ tensorflow_icon }} and PyTorch {{ pytorch_icon }} icons are used to mark whether a backend supports a feature.
+In the documentation, TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, and Paddle {{ paddle_icon }} icons are used to mark whether a backend supports a feature.
 
 ### TensorFlow {{ tensorflow_icon }}
 
@@ -35,6 +35,15 @@ Only the `.savedmodel` format supports C++ inference, which needs the TensorFlow
 The model is device-specific, so that the model generated on the GPU device cannot be run on the CPUs.
 Currently, this backend is developed actively, and has no support for training.
 
+### Paddle {{ paddle_icon }}
+
+- Model filename extensions: `.json` and `.pdiparams`
+- Checkpoint filename extension: `.pd`
+
+[Paddle](https://www.paddlepaddle.org.cn/) version 3.0 or above is required.
+
+The `.pd` extension is used for model checkpoint storage, which is commonly utilized during training and testing in Python. The `.json` extension is for the model's computational graph in [PIR representation](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/paddle_v3_features/paddle_ir_cn.html), while the `.pdiparams` extension stores model parameters. Both `.json` and `.pdiparams` files are exported together and used in model freezing and Python/C++ inference.
+
 ### DP {{ dpmodel_icon }}
 
 :::{note}
@@ -57,7 +66,7 @@ NumPy 1.21 or above is required.
 
 ### Training
 
-When training and freezing a model, you can use `dp --tf` or `dp --pt` in the command line to switch the backend.
+When training and freezing a model, you can use `dp --tf`, `dp --pt` or `dp --pd` in the command line to switch the backend.
 
 ### Inference
 

doc/backend.md

@@ -167,6 +167,7 @@
  "tensorflow_icon": """![TensorFlow](/_static/tensorflow.svg){class=platform-icon}""",
  "pytorch_icon": """![PyTorch](/_static/pytorch.svg){class=platform-icon}""",
  "jax_icon": """![JAX](/_static/jax.svg){class=platform-icon}""",
+ "paddle_icon": """![Paddle](/_static/paddle.svg){class=platform-icon}""",
  "dpmodel_icon": """![DP](/_static/logo_icon.svg){class=platform-icon}""",
 }
 

doc/conf.py

@@ -32,3 +32,26 @@ $ dp --pt freeze -o model_branch1.pth --head CHOSEN_BRANCH
 ```
 
 The output model is called `model_branch1.pth`, which is the specifically frozen model with the `CHOSEN_BRANCH` head.
+
+:::
+
+:::{tab-item} Paddle {{ paddle_icon }}
+
+```bash
+$ dp --pd freeze -o model
+```
+
+in the folder where the model is trained. The output model is called `model.json` and `model.pdiparams`.
+
+In [multi-task mode](../train/multi-task-training-pd.md), you need to choose one available heads (e.g. `CHOSEN_BRANCH`) by `--head`
+to specify which model branch you want to freeze:
+
+```bash
+$ dp --pd freeze -o model_branch1 --head CHOSEN_BRANCH
+```
+
+The output model is called `model_branch1.json`, which is the specifically frozen model with the `CHOSEN_BRANCH` head.
+
+:::
+
+::::

doc/freeze/freeze.md

@@ -186,6 +186,41 @@ Switch to the TensorFlow {{ tensorflow_icon }} tab for more information.
 
 ::::::
 
+::::::{tab-item} Paddle {{ paddle_icon }}
+
+:::::{tab-set}
+
+::::{tab-item} CUDA 12.3
+
+```bash
+pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/
+pip install deepmd-kit
+```
+
+::::
+
+::::{tab-item} CUDA 11.8
+
+```bash
+pip install paddlepaddle-gpu==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
+pip install deepmd-kit
+```
+
+::::
+
+::::{tab-item} CPU
+
+```bash
+pip install paddlepaddle==3.0.0rc0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
+pip install deepmd-kit
+```
+
+::::
+
+:::::
+
+::::::
+
 :::::::
 
 The supported platform includes Linux x86-64 and aarch64 with GNU C Library 2.28 or above, macOS x86-64 and arm64, and Windows x86-64.