Update README.md

Author: JewelRoam

README.md

@@ -44,30 +44,37 @@ python -m graph_net.torch.validate \
 ```
 
 **Example: How does GraphNet extract and construct a computation graph sample on PyTorch?**
+
 <div align="center">
 <img src="/pics/graphnet_sample.png" alt="GraphNet Extract Sample" width="65%">
 </div>
 
+* Source code of custom_op is required **only when** corresponding operator is used in the module, and **no specific format** is required.
+
 **Step 1: graph_net.torch.extract**
 
+Import and wrap the model with `graph_net.torch.extract(name=model_name, dynamic=dynamic_mode)()` is all you need:
+
 ```bash
 import graph_net
 
 # Instantiate the model (e.g. a torchvision model)
 model = ...  
 
 # Extract your own model
-model = graph_net.torch.extract(name="model_name")(model)
+model = graph_net.torch.extract(name="model_name", dynamic="True")(model)
 
 # After running, the extracted graph will be saved to:
 #   $GRAPH_NET_EXTRACT_WORKSPACE/model_name/
 ```
 
-For details, see docstring of `graph_net.torch.extract` defined in `graph_net/torch/extractor.py`
+For details, see docstring of `graph_net.torch.extract` defined in `graph_net/torch/extractor.py`.
 
 **Step 2: graph_net.torch.validate**
+
+To verify that the extracted model meets requirements, we use `graph_net.torch.validate` in CI tool and ask contributors to self-check in advance:
+
 ```bash
-# Verify that the extracted model meets requirements
 python -m graph_net.torch.validate \
   --model-path $GRAPH_NET_EXTRACT_WORKSPACE/model_name
 ```
@@ -76,15 +83,15 @@ python -m graph_net.torch.validate \
 
 **Step 1: Benchmark**
 
-We use ```graph_net/benchmark_demo.sh``` to benchmark GraphNet computation graph samples:
+We use `graph_net/benchmark_demo.sh` to benchmark GraphNet computation graph samples:
 
 ```bash
 bash graph_net/benchmark_demo.sh &
 ```
 
-The script runs ```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:
+Or you can customize and use `graph_net.torch.test_compiler` yourself:
 
 ```bash
 python -m graph_net.torch.test_compiler \
@@ -98,15 +105,15 @@ python -m graph_net.torch.test_compiler \
 # Note: if --compiler is omitted, PyTorch’s built-in compiler is used by default
 ```
 
-After executing, ```graph_net.torch.test_compiler``` will:
+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.
+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.
 
 ```bash
 python -m graph_net.analysis \
@@ -116,7 +123,7 @@ python -m graph_net.analysis \
 
 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.
+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
 
@@ -125,9 +132,7 @@ The script is designed to process a file structure as ```/benchmark_path/compile
 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](https://github.com/PaddlePaddle/ai4c) by enabling large-scale, systematic evaluation of tensor compiler optimizations.
-
-We aim to achieve cross-hardware portability of compiler optimizations by allowing models to learn and transfer optimization strategies. It will significantly reduce the manual effort required to develop efficient operator implementations.
+**Vision**: GraphNet aims to lay the foundation for [ai4c](https://github.com/PaddlePaddle/ai4c) by **enabling large-scale, systematic evaluation** of tensor compiler optimizations, and providing a dataset for **models to learn and transfer optimization strategies**.
 
 ## 💬 GraphNet Community