[docs] update

.github/workflows/test_push.yml

@@ -24,7 +24,7 @@ jobs:
       - name: Install Python dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -r .circleci/requirements.txt
+          pip install -r requirements.txt
 
       - name: Install Other dependencies Package
         run: |

.github/workflows/test_pypi_package.yml

@@ -20,7 +20,7 @@ jobs:
       - name: Install Python dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -r .circleci/requirements.txt
+          pip install -r requirements.txt
 
       - name: Install TensorLayerx
         run: |

CONTRIBUTING.md

@@ -33,6 +33,7 @@ Contribution is always welcomed. Please feel free to open an issue or email to y
   - Zihao Zhao (Gamma Lab)
   - Feng Guo (Gamma Lab)
   - Shujie Li (Gamma Lab)
+  - Jiayi Gao (Gamma Lab)
   - Yuxuan Shan (BUPT)
   - Zeyao Ma (BUPT)
   - Yiming Jia (BUPT)
@@ -49,6 +50,7 @@ Contribution is always welcomed. Please feel free to open an issue or email to y
   - Jiarui Tan (BUPT)
   - Zhaojun Sun (BUPT)
   - Jiayi Guo (BUPT)
+  - Xinyan Zhu (BUPT)
 
   Advisor
 

README.md

@@ -439,20 +439,22 @@ Now, GammaGL supports about 60 models, we welcome everyone to use or contribute
 | [HiD-Net [AAAI 2023]](./examples/hid_net)          | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
 | [FusedGAT [MLSys 2022]](./examples/fusedgat)       |                    | :heavy_check_mark: |                    |                    |
 | [GLNN [ICLR 2022]](./examples/glnn)                | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| [HEAT [T-ITS 2022]](./examples/heat)               |                    | :heavy_check_mark: |                    |                    |
+| [DFAD-GNN [IJCAI 2022]](./examples/dfad_gnn)       |                    | :heavy_check_mark: |                    |                    |
 | [GNN-LF-HF [WWW 2021]](./examples/gnnlfhf)         |                    | :heavy_check_mark: |                    |                    |
+| [DNA [ICLR 2019]](./examples/dna)                  |                    | :heavy_check_mark: |                    |                    |
 
 
-| Contrastive Learning                               | TensorFlow         | PyTorch            | Paddle             | MindSpore          |
-| -------------------------------------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
-| [DGI [ICLR 2019]](./examples/dgi)                  | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| [GRACE [ICML 2020 Workshop]](./examples/grace)     | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| [GRADE [NeurIPS 2022]](./examples/grade)           | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| [MVGRL [ICML 2020]](./examples/mvgrl)              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| [InfoGraph [ICLR 2020]](./examples/infograph)      | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
-| [MERIT [IJCAI 2021]](./examples/merit)             | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: |
-| [GNN-POT [NeurIPS 2023]](./examples/grace_pot)     |                    | :heavy_check_mark: |                    |                    |
-| [MAGCL [AAAI 2023]](./examples/magcl)              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| Contrastive Learning                           | TensorFlow         | PyTorch            | Paddle             | MindSpore          |
+| ---------------------------------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
+| [DGI [ICLR 2019]](./examples/dgi)              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| [GRACE [ICML 2020 Workshop]](./examples/grace) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| [GRADE [NeurIPS 2022]](./examples/grade)       | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| [MVGRL [ICML 2020]](./examples/mvgrl)          | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| [InfoGraph [ICLR 2020]](./examples/infograph)  | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| [MERIT [IJCAI 2021]](./examples/merit)         | :heavy_check_mark: |                    | :heavy_check_mark: | :heavy_check_mark: |
+| [GNN-POT [NeurIPS 2023]](./examples/grace_pot) |                    | :heavy_check_mark: |                    |                    |
+| [MAGCL [AAAI 2023]](./examples/magcl)          | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
+| [Sp2GCL [NeurIPS 2023]](./examples/sp2gcl)     |                    | :heavy_check_mark: |                    |                    |
 
 | Heterogeneous Graph Learning                       | TensorFlow         | PyTorch            | Paddle             | MindSpore          |
 | -------------------------------------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
@@ -466,6 +468,8 @@ Now, GammaGL supports about 60 models, we welcome everyone to use or contribute
 | [MetaPath2Vec [KDD 2017]](./examples/metapath2vec) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
 | [HERec [TKDE 2018]](./examples/herec)              | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
 | [HeCo [KDD 2021]](./examples/heco)                 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |
+| [DHN [TKDE 2023]](./examples/dhn)                  |                    | :heavy_check_mark: |                    |                    |
+| [HEAT [T-ITS 2023]](./examples/heat)               |                    | :heavy_check_mark: |                    |                    |
 
 > Note
 > 

docs/source/notes/installation.rst

@@ -60,7 +60,7 @@ Install from pip
 
 .. code:: bash
 
-    pip install gammgl
+    pip install gammagl
 
 Install from source
 -------------------
@@ -114,7 +114,7 @@ Install from source
 
 .. code:: bash
 
-   python setup.py bulid_ext --inplace
+   python setup.py build_ext --inplace
 
 How to Run
 ----------

profiler/mpops/complete_test/README.md

@@ -4,7 +4,7 @@ In order for users to clearly understand the details of operator testing, we hav
 
 ## Dataset
 
-The location of the dataset is `GammaGL/profiler/mpops/edge_index`, `cora.npy` corresponds to the `Cora` dataset, `pubmed.npy` corresponds to the `PubMed` dataset, `ogbn-arxiv.npy` corresponds to the `Ogbn-Arxiv` dataset. 
+You need to run the `test.py` in the `edge_index/` path to download the dataset. The location of the dataset is `GammaGL/profiler/mpops/edge_index`, `cora.npy` corresponds to the `Cora` dataset, `pubmed.npy` corresponds to the `PubMed` dataset, `ogbn-arxiv.npy` corresponds to the `Ogbn-Arxiv` dataset. 
 
 You can use `np.load(path_to_the_dataset)` to load the corresponding dataset.
 
@@ -14,55 +14,40 @@ The information of the dataset is as follows:
 | :--------: | :-------------: | :-------------: |
 |    Cora    |      2708       |      13264      |
 |   PubMed   |      19717      |     108368      |
-| Ogbn-Arxiv |     169344      |     2315598     |
+| Ogbn-Arxiv |     169343      |     2315598     |
 
 ## Directory of the test code folder
 
 The location of the test code is `GammaGL/profiler/mpops/complete_test`. The following is the directory structure of the `complete_test` folder, `:` followed by the file description. 
 
 ```
 complete_test
-├── mp_cpu: code folder to test the efficiency of message passing process under CPU
-│   ├── dgl_mp_cpu.py
-│   ├── ms_mp_cpu.py
-│   ├── pd_ext_sum_cpu.py
-│   ├── pd_mp_cpu.py
-│   ├── pyg_mp_cpu.py
-│   ├── spmm_sum_cpu.py
-│   ├── tf_mp_cpu.py
-│   ├── th_ext_max_cpu.py
-│   └── th_mp_cpu.py
-├── mp_gpu: code folder to test the efficiency of message passing process under GPU
-│   ├── dgl_mp_gpu.py
-│   ├── ms_mp_gpu.py
-│   ├── pd_ext_sum_gpu.py
-│   ├── pd_mp_gpu.py
-│   ├── pyg_mp_gpu.py
-│   ├── spmm_sum_gpu.py
-│   ├── tf_mp_gpu.py
-│   ├── th_ext_max_gpu.py
-│   └── th_mp_gpu.py
-├── ops_cpu: code folder for testing the efficiency of operators under CPU
-│   ├── ms_segment_ops_cpu.py
-│   ├── pd_ext_segment_sum_cpu.py
-│   ├── pd_segment_ops_cpu.py
-│   ├── pyg_scatter_ops_cpu.py
-│   ├── tf_segment_ops_cpu.py
-│   ├── th_ext_segment_max_cpu.py
-│   └── th_segment_ops_cpu.py
-└── ops_gpu: code folder for testing the efficiency of operators under GPU
-	├── ms_segment_ops_gpu.py
-    ├── pd_ext_segment_sum_gpu.py
-    ├── pd_segment_ops_gpu.py
-    ├── pyg_scatter_ops_gpu.py
-    ├── tf_segment_ops_gpu.py
-    ├── th_ext_segment_max_gpu.py
-    └── th_segment_ops_gpu.py
+|-- README.md
+|-- mp_cpu
+|   |-- dgl_mp_cpu.py
+|   |-- ggl_mp_cpu.py
+|   |-- pd_ext_sum_cpu.py
+|   |-- pyg_mp_cpu.py
+|   `-- spmm_sum_cpu.py
+|-- mp_gpu
+|   |-- dgl_mp_gpu.py
+|   |-- ggl_mp_gpu.py
+|   |-- pd_ext_sum_gpu.py
+|   |-- pyg_mp_gpu.py
+|   `-- spmm_sum_gpu.py
+|-- ops_cpu
+|   |-- ggl_segment_cpu.py
+|   |-- pd_ext_segment_sum_cpu.py
+|   `-- pyg_scatter_ops_cpu.py
+`-- ops_gpu
+    |-- ggl_segment_gpu.py
+    |-- pd_ext_segment_sum_gpu.py
+    `-- pyg_scatter_ops_gpu.py
 ```
 
 ## How to run test code
 
-Let's take `th_segment_ops_gpu.py` as an example. 
+Let's take `ggl_segment_gpu.py` as an example. 
 
 You can switch the program running position through `os.environ["CUDA_VISIBLE_DEVICES"] = "4"` , the `4` represents the fourth GPU in your server, and if you set it to `-1` , it will run on the CPU. 
 
@@ -81,9 +66,9 @@ You can set the dataset to run by changing the list of `file_name` .
 
 In order to accurately calculate the running time, we increase the running times of each operator. After getting the running time, we divide it by the running times to get the single running time of the operator. `iter` represents the number of times each operator runs.
 
-For `th_segment_ops_gpu.py` , you can run it using:
+For `ggl_segment_gpu.py` , you can run it using:
 
 ```bash
-python th_segment_ops_gpu.py
+python ggl_segment_gpu.py
 ```
 

profiler/mpops/complete_test/mp_cpu/ggl_mp_cpu.py

@@ -0,0 +1,82 @@
+import os
+# os.environ['TL_BACKEND'] = "torch"
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+import tensorlayerx as tlx
+from gammagl.mpops import *
+import numpy as np
+import time
+
+relative_path = '/home/zgy/review/zgy/GammaGL/profiler/mpops/edge_index/'
+file_name = ['cora.npy', 'pubmed.npy', 'ogbn-arxiv.npy']
+embedding = [16, 64, 256]
+iter = 10
+
+operations = {
+    'segment_max': segment_max,
+    'unsorted_segmnet_max': unsorted_segment_max,
+    'segment_sum': segment_sum,
+    'unsorted_segment_sum': unsorted_segment_sum,
+    'segment_mean': segment_mean,
+    'unsorted_segment_mean': unsorted_segment_mean
+}
+
+
+def run_test(operation, x, dst, num_nodes):
+    edge_weight = tlx.ones(shape=(edge_index.shape[1],), dtype=tlx.float32)
+    edge_weight = tlx.expand_dims(edge_weight, -1)
+
+    msg = tlx.gather(x, src) # warm up
+    operation(msg, dst, num_nodes)
+
+    start = time.time()
+    for j in range(iter):
+        msg = tlx.gather(x, src)
+        msg = msg * edge_weight
+        operation(msg, dst, num_nodes)
+    end = time.time()
+    return end - start
+
+
+with open('test_results.txt', 'w') as result_file:
+    for name in file_name:
+        path = relative_path + name
+        result_file.write(f"Loading data from {path}\n")
+        print(f"Loading data from {path}")
+        edge_index = np.load(path)
+
+        num_nodes = np.max(edge_index) + 1
+        src = tlx.convert_to_tensor(edge_index[0, :], tlx.int64)
+        dst = tlx.convert_to_tensor(edge_index[1, :], tlx.int64)
+        dst2 = None
+
+        # TensorFlow-specific preprocessing for sorted operations
+        if tlx.BACKEND == 'tensorflow' or tlx.BACKEND == 'paddle' or tlx.BACKEND == 'mindspore':
+            dst_numpy = tlx.convert_to_numpy(dst)
+            idx = np.argsort(dst_numpy)
+            dst2 = tlx.gather(tlx.convert_to_tensor(dst_numpy, dtype=tlx.int64), tlx.convert_to_tensor(idx, dtype=tlx.int64))
+
+        for embedding_dim in embedding:
+            result_file.write(f"** Testing embedding dimension {embedding_dim} **\n")
+            print(f"** Testing embedding dimension {embedding_dim} **")
+            x = tlx.convert_to_tensor(np.random.randn(num_nodes, embedding_dim), dtype=tlx.float32)
+
+            # Running tests for all operations
+            for op_name, op_func in operations.items():
+                dst_target = dst2 if dst2 is not None else dst
+                duration = run_test(op_func, x, dst_target, num_nodes)
+                result = f"{op_name}: {duration:.3f} seconds\n"
+                result_file.write(result)
+                print(result)
+
+            print(f"** Done testing embedding dimension {embedding_dim} **")
+            result_file.write(f"** Done testing embedding dimension {embedding_dim} **\n")
+
+        if tlx.BACKEND == 'paddle':
+            info = f"Data tensors are on device: {x.place}\n"
+        elif tlx.BACKEND == 'mindspore':
+            info = f"mindspore\n"
+        else:
+            info = f"Data tensors are on device: {x.device}\n"
+        print(info, end='')
+        result_file.write(info)
+