diff --git a/README.md b/README.md
index 8f4ba187..d8017d0a 100644
--- a/README.md
+++ b/README.md
@@ -21,18 +21,20 @@ We summarize the contributions of CogDL as follows:
 
 ## ❗ News
 
+- The new **v0.4.0 release** refactors the data storage (from `Data` to `Graph`) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see [this link](https://kdd2021graph.github.io/) for more details. 🎉
+
 - CogDL supports GNN models with Mixture of Experts (MoE). You can install [FastMoE](https://github.com/laekov/fastmoe) and try **[MoE GCN](./cogdl/models/nn/moe_gcn.py)** in CogDL now!
 
 - The new **v0.3.0 release** provides a fast spmm operator to speed up GNN training. We also release the first version of **[CogDL paper](https://arxiv.org/abs/2103.00959)** in arXiv. You can join [our slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg) for discussion. 🎉🎉🎉
 
-- The new **v0.2.0 release** includes easy-to-use `experiment` and `pipeline` APIs for all experiments and applications. The `experiment` API supports automl features of searching hyper-parameters. This release also provides `OAGBert` API for model inference (`OAGBert` is trained on large-scale academic corpus by our lab). Some features and models are added by the open source community (thanks to all the contributors 🎉).
-
 <details>
 <summary>
 News History
 </summary>
 <br/>
 
+- The new **v0.2.0 release** includes easy-to-use `experiment` and `pipeline` APIs for all experiments and applications. The `experiment` API supports automl features of searching hyper-parameters. This release also provides `OAGBert` API for model inference (`OAGBert` is trained on large-scale academic corpus by our lab). Some features and models are added by the open source community (thanks to all the contributors 🎉).
+
 - The new **v0.1.2 release** includes a pre-training task, many examples, OGB datasets, some knowledge graph embedding methods, and some graph neural network models. The coverage of CogDL is increased to 80%. Some new APIs, such as `Trainer` and `Sampler`, are developed and being tested. 
 
 - The new **v0.1.1 release** includes the knowledge link prediction task, many state-of-the-art models, and `optuna` support. We also have a [Chinese WeChat post](https://mp.weixin.qq.com/s/IUh-ctQwtSXGvdTij5eDDg) about the CogDL release.
diff --git a/README_CN.md b/README_CN.md
index 4dbd4c29..437d02f7 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -21,18 +21,20 @@ CogDL的特性包括：
 
 ## ❗ 最新
 
+- 最新的 **v0.4.0版本** 重构了底层的数据存储（从`Data`类变为`Graph`类），并且提供了更多快速的算子来加速图神经网络的训练。这个版本还包含了很多图自监督学习的算法。同时，我们很高兴地宣布我们将在8月份的KDD 2021会议上给一个CogDL相关的tutorial。具体信息请参见[这个链接](https://kdd2021graph.github.io/). 🎉
+
 - CogDL支持图神经网络模型使用混合专家模块（Mixture of Experts, MoE）。 你可以安装[FastMoE](https://github.com/laekov/fastmoe)然后在CogDL中尝试 **[MoE GCN](./cogdl/models/nn/moe_gcn.py)** 模型!
 
 - 最新的 **v0.3.0版本** 提供了快速的稀疏矩阵乘操作来加速图神经网络模型的训练。我们在arXiv上发布了 **[CogDL paper](https://arxiv.org/abs/2103.00959)** 的初版. 你可以加入[我们的slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg)来讨论CogDL相关的内容。🎉
 
-- 最新的 **v0.2.0版本** 包含了非常易用的`experiment`和`pipeline`接口，其中`experiment`接口还支持超参搜索。这个版本还提供了`OAGBert`模型的接口（`OAGBert`是我们实验室推出的在大规模学术语料下训练的模型）。这个版本的很多内容是由开源社区的小伙伴们提供的，感谢大家的支持！🎉
-
 <details>
 <summary>
 历史
 </summary>
 <br/>
 
+- 最新的 **v0.2.0版本** 包含了非常易用的`experiment`和`pipeline`接口，其中`experiment`接口还支持超参搜索。这个版本还提供了`OAGBert`模型的接口（`OAGBert`是我们实验室推出的在大规模学术语料下训练的模型）。这个版本的很多内容是由开源社区的小伙伴们提供的，感谢大家的支持！🎉
+
 - 最新的 **v0.1.2版本** 包括了预训练任务、各种使用样例、OGB数据集、知识图谱表示学习算法和一些图神经网络模型。CogDL的测试覆盖率增加至80%。正在开发和测试一些新的API，比如`Trainer`和`Sampler`。
 
 - 最新的 **v0.1.1版本** 包括了知识图谱链接预测任务、很多前沿的模型，支持使用`optuna`进行超参搜索。我们同时发布了一篇[推送](https://mp.weixin.qq.com/s/IUh-ctQwtSXGvdTij5eDDg)来介绍CogDL。
diff --git a/cogdl/__init__.py b/cogdl/__init__.py
index 4700b772..b2aa599e 100644
--- a/cogdl/__init__.py
+++ b/cogdl/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.3.0.post1"
+__version__ = "0.4.0"
 
 from .experiments import experiment
 from .oag import oagbert
diff --git a/cogdl/operators/mhspmm.py b/cogdl/operators/mhspmm.py
index d48dd324..a9b1afcf 100644
--- a/cogdl/operators/mhspmm.py
+++ b/cogdl/operators/mhspmm.py
@@ -32,7 +32,7 @@
     def csrmhspmm(rowptr, colind, feat, attention):
         return MHSPMMFunction.apply(rowptr, colind, feat, attention)
 
-    print("Use fast Multi-head attention...")
+
 except Exception:
     mhspmm = None
     csrmhspmm = None