Update README

README.md

@@ -1,7 +1,6 @@
 # GraphNet  ![](https://img.shields.io/badge/version-v0.1-brightgreen) ![](https://img.shields.io/github/issues/PaddlePaddle/GraphNet?label=open%20issues)    [![](https://img.shields.io/badge/Contribute%20to%20GraphNet-blue)](https://github.com/PaddlePaddle/GraphNet/issues/98)
 
-
-**GraphNet** is a large-scale dataset of deep learning **computation graphs**, built as a standard benchmark for **tensor compiler** optimization. It provides 2.7K computation graphs extracted from state-of-the-art deep learning models spanning diverse tasks and ML frameworks. With standardized formats and rich metadata, GraphNet enables fair comparison and reproducible evaluation of the general optimization capabilities of tensor compilers, thereby supporting advanced research in AI for compilers (**AI4C**).
+**GraphNet** is a large-scale dataset of deep learning **computation graphs**, built as a standard benchmark for **tensor compiler** optimization. It provides 2.7K computation graphs extracted from state-of-the-art deep learning models spanning diverse tasks and ML frameworks. With standardized formats and rich metadata, GraphNet enables fair comparison and reproducible evaluation of the general optimization capabilities of tensor compilers, thereby supporting advanced research such as AI for System on compilers ([**ai4c**](https://github.com/PaddlePaddle/ai4c)).
 
 <br>
 <div align="center">
@@ -10,9 +9,7 @@
 
 Compiler developers can use GraphNet samples to evaluate tensor compilers (e.g., CINN, TorchInductor, TVM) on target tasks. The figure above shows the speedup of two compilers (CINN and TorchInductor) across two tasks (CV and NLP).
 
-
-
-## Dataset Construction
+## 🧱 Dataset Construction
 
 To guarantee the dataset’s overall quality, reproducibility, and cross-compiler compatibility, we define the following construction **constraints**:
 
@@ -22,7 +19,6 @@ To guarantee the dataset’s overall quality, reproducibility, and cross-compile
 4. Operator names within each computation graph must be statically parseable.
 5. If custom operators are used, their implementation code must be fully accessible.
 
-
 ### Graph Extraction & Validation
 
 We provide automated extraction and validation tools for constructing this dataset.
@@ -31,26 +27,25 @@ We provide automated extraction and validation tools for constructing this datas
 <img src="/pics/graphnet_overview.jpg" alt="GraphNet Architecture Overview" width="65%">
 </div>
 
-
 **Demo: Extract & Validate ResNet‑18**
-```
+```bash
 git clone https://github.com/PaddlePaddle/GraphNet.git
 cd GraphNet
 
 # Set your workspace directory
-export GRAPH_NET_EXTRACT_WORKSPACE=/home/yourname/graphnet_workspace
+export GRAPH_NET_EXTRACT_WORKSPACE=/home/yourname/graphnet_workspace/
 
 # Extract the ResNet‑18 computation graph
 python graph_net/test/vision_model_test.py
 
-# Validate the extracted graph (e.g. /home/yourname/graphnet_workspace/resnet18)
+# Validate the extracted graph (e.g. /home/yourname/graphnet_workspace/resnet18/)
 python -m graph_net.torch.validate \
-  --model-path $GRAPH_NET_EXTRACT_WORKSPACE/resnet18
+  --model-path $GRAPH_NET_EXTRACT_WORKSPACE/resnet18/
 ```
 
-**graph_net.torch.extract**
+**Step 1: graph_net.torch.extract**
 
-```python
+```bash
 import graph_net
 
 # Instantiate the model (e.g. a torchvision model)
@@ -60,71 +55,76 @@ model = ...
 model = graph_net.torch.extract(name="model_name")(model)
 
 # After running, the extracted graph will be saved to:
-#   $GRAPH_NET_EXTRACT_WORKSPACE/model_name
+#   $GRAPH_NET_EXTRACT_WORKSPACE/model_name/
 ```
 
 For details, see docstring of `graph_net.torch.extract` defined in `graph_net/torch/extractor.py`
 
-**graph_net.torch.validate**
-```
+**Step 2: graph_net.torch.validate**
+```bash
 # Verify that the extracted model meets requirements
 python -m graph_net.torch.validate \
   --model-path $GRAPH_NET_EXTRACT_WORKSPACE/model_name
 ```
 
-
-## Compiler Evaluation
-
-**Demo: How to benchmark your compiler on the model:**
+## ⚖️ Compiler Evaluation
 
 **Step 1: Benchmark**
 
 We use ```graph_net/benchmark_demo.sh``` to benchmark GraphNet computation graph samples:
 
-```
+```bash
 bash graph_net/benchmark_demo.sh &
 ```
 
-The script will run ```graph_net.torch.test_compiler``` with specific batch and log configurations.
+The script runs ```graph_net.torch.test_compiler``` with specific batch and log configurations.
 
 Or you can customize and use ```graph_net.torch.test_compiler``` yourself:
 
-```
-python3 -m graph_net.torch.test_compiler \
+```bash
+python -m graph_net.torch.test_compiler \
   --model-path $GRAPH_NET_EXTRACT_WORKSPACE/model_name/ \
-  --compiler /path/to/custom/compiler/ \
+  --compiler /custom/or/builtin/compiler/ \
+  --warmup /times/to/warmup/ \
+  --trials /times/to/test/ \
+  --device /device/to/execute/ \
   --output-dir /path/to/save/JSON/result/file/
+
 # Note: if --compiler is omitted, PyTorch’s built-in compiler is used by default
 ```
 
+After executing, ```graph_net.torch.test_compiler``` will:
+1. Running the original model in eager mode to record a baseline.
+2. Compiling the model with the specified backend (e.g., CINN, TVM, Inductor, TensorRT, XLA, BladeDISC).
+3. Executing the compiled model and collecting its runtime and outputs.
+4. Conduct speedup by comparing the compiled results against the baseline.
+
 **Step 2: Analysis**
 
 After processing, we provide ```graph_net/analysis.py``` to generate [violin plot](https://en.m.wikipedia.org/wiki/Violin_plot) based on the JSON results.
 
-```
-python3 graph_net/analysis.py \
+```bash
+python -m graph_net.analysis \
   --benchmark-path /path/to/read/JSON/result/file/ \
   --output-dir /path/to/save/output/figures/
 ```
 
-After executing, one summary plot of results on all compilers (as shown below in "Evaluation Results Example"), as well as multiple sub-plots of results in categories (model tasks, Library...) on a single compiler. 
+After executing, one summary plot of results on all compilers, as well as multiple sub-plots of results in categories (model tasks, Library...) on a single compiler will be exported. 
 
 The script is designed to process a file structure as ```/benchmark_path/compiler_name/category_name/``` (for example ```/benchmark_logs/paddle/nlp/```), and items on x-axis are identified by name of the folders. So you can modify  ```read_all_speedups``` function to fit the benchmark settings on your demand.
 
-## Roadmap
+## 📌 Roadmap
 
 1. Scale GraphNet to 10K+ graphs.
 2. Further annotate GraphNet samples into more granular sub-categories
 3. Extract samples from multi-GPU scenarios to support benchmarking and optimization for large-scale, distributed computing.
 4. Enable splitting full graphs into independently optimized subgraphs and operator sequences.
 
-**Vision**: GraphNet aims to lay the foundation for AI4C by enabling large-scale, systematic evaluation of tensor compiler optimizations.
-
-## GraphNet Community:
+**Vision**: GraphNet aims to lay the foundation for [ai4c](https://github.com/PaddlePaddle/ai4c) by enabling large-scale, systematic evaluation of tensor compiler optimizations.
 
+## 💬 GraphNet Community
 
-You can join GraphNet community via the following group chats.
-
+You can join our community via following group chats. Welcome to ask any questions about using and building GraphNet.
 
 <div align="center">
 <table>
@@ -140,8 +140,5 @@ You can join GraphNet community via the following group chats.
 </table>
 </div>
 
-
-
-##  License
+## 🪪 License
 This project is released under the [MIT License](LICENSE).
-