[sglang] Feat: Search Tool Invocation in Multi-Turn RL Training

.github/workflows/sgl.yml

@@ -86,3 +86,7 @@ jobs:
         run: |
           cd tests/workers/rollout
           pytest -s test_sglang_async_rollout_sf_tools.py
+      - name: Test the latest SGLang Rollout async with search tool
+        run: |
+          cd tests/workers/rollout
+          pytest -s test_sglang_async_rollout_search_tools.py

docs/sglang_multiturn/multiturn.rst

@@ -1,8 +1,8 @@
 Multi-turn Rollout Support
-=========================
+==========================
 
 Basic Configuration
-~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~
 
 To enable multi-turn rollout, make sure to configure the following fields in your rollout configuration:
 
@@ -16,7 +16,7 @@ To enable multi-turn rollout, make sure to configure the following fields in you
 These configuration activates the sglang_async engine for multi-turn interaction during rollout.
 
 Custom Tool Configuration
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 For custom environment interaction tools, you can implement your own tools based on ``verl.tools.base_tool.BaseTool``. Then, specify your tool configurations in a YAML file:
 
@@ -41,7 +41,7 @@ Finally, set the ``tools_config_file`` in your rollout config:
 This allows integration of customized tool behaviors during actor rollout steps. 
 
 GSM8K Multi-turn Training Performance  
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 See the training performance of multi-turn rollout on the GSM8K task HERE_.
 
@@ -50,3 +50,11 @@ See the training performance of multi-turn rollout on the GSM8K task HERE_.
 .. _GSM8KTool_example_configuration: https://github.com/volcengine/verl/blob/main/examples/sglang_multiturn/config/tool_config/gsm8k_tool_config.yaml
 
 .. _gsm8k_tool.py: https://github.com/volcengine/verl/blob/main/verl/tools/gsm8k_tool.py
+
+Search Tool Integration
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. toctree::
+   :maxdepth: 1
+
+   search_tool_example

docs/sglang_multiturn/search_tool_example.rst

@@ -0,0 +1,261 @@
+=======================
+Search Tool Integration
+=======================
+Introduction
+------------
+- We have added a search tool calling function to Multi-Turn RL, enabling the model to initiate retrieval requests during Actor rollout and directly use retrieval results for training. **We support using a local dense retriever as the retrieval tool, as well as integrating with your own local retrieval engine.**
+
+
+
+Quick Reproduction
+------------------
+
+Create a New Docker Container
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+   docker run \
+       -it \
+       --shm-size 32g \
+       --gpus all \
+       -v {Huggingface-Cache-Path}:/root/.cache \
+       --ipc=host \
+       --network=host \
+       --privileged \
+       --name sglang_{your-name} \
+       lmsysorg/sglang:dev \
+       /bin/zsh
+
+If you need to restart after exiting the container:
+
+.. code:: bash
+
+   docker start -i sglang_{your-name}
+
+Update Python and Configure the Virtual Environment using uv
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+   apt update
+   apt install -y python3.10 python3.10-venv
+
+   # Create a virtual environment
+   python3 -m venv ~/.python/verl-multiturn-rollout
+
+   # Activate the virtual environment
+   source ~/.python/verl-multiturn-rollout/bin/activate
+
+   # Install uv
+   python3 -m pip install uv
+
+Install verl Upstream
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+   cd ~
+   git clone https://github.com/volcengine/verl.git
+   cd verl
+
+   # Install verl
+   python3 -m uv pip install .
+   python3 -m uv pip install -r ./requirements_sglang.txt
+
+   # Manually install flash-attn
+   python3 -m uv pip install wheel
+   python3 -m uv pip install packaging
+   python3 -m uv pip install flash-attn --no-build-isolation --no-deps
+
+Set Up a Local Retrieval Engine
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you are using your own local retrieval service, you can skip this
+step. We chose the local dense retriever provided in the search-R1
+example; detailed instructions are in the `searchR1
+docs <https://raw.githubusercontent.com/PeterGriffinJin/Search-R1/refs/heads/main/docs/retriever.md>`__.
+In brief:
+
+-  The GPU version offers higher accuracy and speed; each GPU uses about
+   5–7 GB of memory.
+-  The CPU version can be used for simple testing but has lower
+   retrieval precision, which will degrade training performance. See the
+   `retriever
+   documentation <https://github.com/PeterGriffinJin/Search-R1/blob/main/docs/retriever.md>`__
+   in search-R1 for details.
+-  Recommend using Conda to install faiss-gpu=1.8.0; venv may cause errors.
+
+**Note**: To start both the training process and the local retrieval
+service, we launch two separate Python environments. The training uses
+uv in the verl-multiturn-rollout environment, while the retriever uses
+conda to install ``faiss-gpu``.
+
+.. code:: bash
+
+   # Download the Miniconda installer script
+   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
+
+   # Install to $HOME/miniconda3 in batch mode
+   bash ~/miniconda.sh -b -p $HOME/miniconda3
+
+   # Activate conda (only in the current shell)
+   eval "$($HOME/miniconda3/bin/conda shell.bash hook)"
+
+   # (Optional) Add conda to your default shell startup
+   conda init
+
+   # Reload shell config
+   source ~/.bashrc
+
+   # Create and activate the retriever environment with Python 3.10
+   conda create -n retriever python=3.10 -y
+   conda activate retriever
+
+   # Install PyTorch (with GPU support) and related libraries
+   conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y
+
+   # Install other Python packages
+   pip install transformers datasets pyserini huggingface_hub
+
+   # Install the GPU version of faiss
+   conda install faiss-gpu=1.8.0 -c pytorch -c nvidia -y
+
+   # Install the API service framework
+   pip install uvicorn fastapi
+
+Download the Indexing and Corpus
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The local retrieval files are large—prepare sufficient disk space.
+Downloading is about 60–70 GB, and uncompressed takes about 132 GB:
+
+.. code:: bash
+
+   conda activate retriever
+
+   save_path=/the/path/to/save
+   python examples/sglang_multiturn/search_r1_like/local_dense_retriever/download.py --save_path $save_path
+   cat $save_path/part_* > $save_path/e5_Flat.index
+   gzip -d $save_path/wiki-18.jsonl.gz
+
+Start the Local flat e5 Retrieval Server
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. The first startup will download models and load the index.
+2. Apart from the download, startup takes about 1–2 minutes.
+3. After startup, each GPU uses about 5–7 GB of memory, leaving the rest
+   for multi-turn RL training.
+
+.. code:: bash
+
+   conda activate retriever
+
+   index_file=$save_path/e5_Flat.index
+   corpus_file=$save_path/wiki-18.jsonl
+   retriever_name=e5
+   retriever_path=intfloat/e5-base-v2
+
+   python examples/sglang_multiturn/search_r1_like/local_dense_retriever/retrieval_server.py \
+     --index_path $index_file \
+     --corpus_path $corpus_file \
+     --topk 3 \
+     --retriever_name $retriever_name \
+     --retriever_model $retriever_path \
+     --faiss_gpu
+
+Set Up WANDB_API_KEY
+~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+   export WANDB_API_KEY={YOUR_WANDB_API_KEY}
+
+   # Define a timestamp function
+   function now() {
+       date '+%Y-%m-%d-%H-%M'
+   }
+
+**Preprocess the Dataset**
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+   **Note:** The following data processing and training commands must be
+   run in the verl-multiturn-rollout environment.
+
+.. code:: bash
+
+   python3 examples/data_preprocess/preprocess_search_r1_dataset.py
+
+Testing on 8 x H20
+~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+   # Ensure the now() function is defined
+   # Create a logs directory
+   mkdir -p logs
+
+   # Set GPUs and run with a suitable log path
+   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+
+   nohup bash examples/sglang_multiturn/search_r1_like/run_qwen2.5-3b_instruct_search_multiturn.sh \
+     trainer.experiment_name=qwen2.5-3b-it_rm-searchR1-like-sgl-multiturn-$(now) \
+     > logs/searchR1-like$(now).log 2>&1 &
+
+Custom Search Configuration
+---------------------------
+
+To enable multi-turn reasoning, set the following fields in your config:
+
+.. code:: yaml
+
+   actor_rollout_ref:
+     rollout:
+       name: "sglang_async"
+       multi_turn:
+         enable: True
+
+You must specify ``retrieval_service_url`` in ``examples/sglang_multiturn/config/tool_config/search_tool_config.yaml``, and properly configure concurrency. For more details on concurrency, refer to the Sandbox Fusion example:
+
+.. code:: yaml
+
+   tools:
+     - class_name: verl.tools.search_tool.SearchTool
+       config:
+         retrieval_service_url: http://127.0.0.1:8000/retrieve
+         num_workers: 120
+         rate_limit: 120
+         timeout: 30
+
+The retriever input/output formats are as follows. If your service
+parameters match, only modify ``retrieval_service_url``. You can also
+customize in ``search_r1_like_utils.py``.
+
+.. code:: python
+
+   Input format:
+   {
+     "queries": ["What is Python?", "Tell me about neural networks."],
+     "topk": 3,
+     "return_scores": true
+   }
+
+   Output format (when return_scores=True, similarity scores are returned):
+   {
+       "result": [
+           [   # Results for each query
+               {
+                   "document": doc, "score": score
+               },
+               # ... more documents
+           ],
+           # ... results for other queries
+       ]
+   }
+
+Notes
+-----
+
+1. The total training time is about 27 hours; meanwhile, the validation
+   dataset is very large (51 k), and each validation takes about 6000 s.
+   (Therefore, ``val_before_train=False`` by default)