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Jian Zhang (James) edited this page May 17, 2023 · 15 revisions

Use Your Own Data Tutorial#

It is easy for users to prepare their own graph data and leverage GraphStorm’s built-in GNN models, e.g., RGCN and RGAT, to perform GML tasks. It takes three steps to use your own graph data in GraphStorm:

  • Step 1: Prepare your own graph data in the required format.

  • Step 2: Modify the GraphStorm configuration YAML file.

  • Step 3: Launch GraphStorm commands for training/inference.

Note

All commands below should be run in a GraphStorm Docker container. Please refer to the GraphStorm Docker environment setup to prepare your environment.

Step 1: Prepare Your Own Graph Data#

There are two options to prepare your own graph data for using GraphStorm:

  • Prepare your graph in the required raw data format, and use GraphStorm’s construction tools to automatically generate the input files. This is the preferred method.

  • Prepare your data as a DGL heterogeneous graph following the required format, and then use GraphStorm’s partition tools to generate the input files. This option is for experienced DGL users.

Option 1: Required raw data format#

GraphStorm provides a graph construction tool to generate input files for using the training/inference commands. The detailed information about the raw data format can be found in the Graph Construction Configurations.

In general, the graph construction tool needs three sets of files as inputs.

  • A configuration JSON file, which describes the graph structure, i.e. nodes and edges, the tasks to perform, the node features, and data file paths.

  • A set of raw node data files. Each type of nodes must have one file associated. If the file is too big, users can split this one file into multiple files that have the same columns and different rows.

  • A set of raw edge data files. Each type of edges must have one file associated. If the file is too big, users can split this one file into multiple files that have the same columns and different rows.

This tutorial uses the ACM publication graph as a demonstration to show how to prepare your own graph data, and what these files and their contents are like.

First go the /graphstorm/examples/ folder.

cd /graphstorm/examples

Then run the command to create the ACM data with the required raw format.

python3 -m /graphstorm/examples/acm_data.py --output-path /tmp/acm_raw

Once succeeded, the command will create a set of folders and files under the /tmp/acm_raw/ folder, as shown below:

/tmp/acm_raw
config.json
|- edges
    author_writing_paper.parquet
    paper_cited_paper.parquet
    paper_citing_paper.parquet
    paper_is-about_subject.parquet
    paper_written-by_author.parquet
    subject_has_paper.parquet
|- nodes
    author.parquet
    paper.parquet
    subject.parquet

The input configuration JSON#

GraphStorm’s graph construction tool relies on the configuration JSON to provide graph information. Explanations of the format of the configuration JSON contents could be found in the GraphStorm Graph Configuration JSON. Below show the contents of the examplary ACM config.json file.

{
    "nodes": [
        {
            "node_type": "author",
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/nodes/author.parquet"
            ],
            "node_id_col": "node_id",
            "features": [
                {
                    "feature_col": "feat",
                    "feature_name": "feat"
                }
            ]
        },
        {
            "node_type": "paper",
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/nodes/paper.parquet"
            ],
            "node_id_col": "node_id",
            "features": [
                {
                    "feature_col": "feat",
                    "feature_name": "feat"
                }
            ],
            "labels": [
                {
                    "label_col": "label",
                    "task_type": "classification",
                    "split_pct": [
                        0.8,
                        0.1,
                        0.1
                    ]
                }
            ]
        },
        {
            "node_type": "subject",
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/nodes/subject.parquet"
            ],
            "node_id_col": "node_id",
            "features": [
                {
                    "feature_col": "feat",
                    "feature_name": "feat"
                }
            ]
        }
    ],
    "edges": [
        {
            "relation": [
                "author",
                "writing",
                "paper"
            ],
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/edges/author_writing_paper.parquet"
            ],
            "source_id_col": "source_id",
            "dest_id_col": "dest_id"
        },
        {
            "relation": [
                "paper",
                "cited",
                "paper"
            ],
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/edges/paper_cited_paper.parquet"
            ],
            "source_id_col": "source_id",
            "dest_id_col": "dest_id"
        },
        {
            "relation": [
                "paper",
                "citing",
                "paper"
            ],
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/edges/paper_citing_paper.parquet"
            ],
            "source_id_col": "source_id",
            "dest_id_col": "dest_id"
        },
        {
            "relation": [
                "paper",
                "is-about",
                "subject"
            ],
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/edges/paper_is-about_subject.parquet"
            ],
            "source_id_col": "source_id",
            "dest_id_col": "dest_id"
        },
        {
            "relation": [
                "paper",
                "written-by",
                "author"
            ],
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/edges/paper_written-by_author.parquet"
            ],
            "source_id_col": "source_id",
            "dest_id_col": "dest_id"
        },
        {
            "relation": [
                "subject",
                "has",
                "paper"
            ],
            "format": {
                "name": "parquet"
            },
            "files": [
                "/tmp/acm_raw/edges/subject_has_paper.parquet"
            ],
            "source_id_col": "source_id",
            "dest_id_col": "dest_id"
        }
    ]
}

Based on the original ACM dataset, this example builds a simple heterogenous graph that contains three types of nodes and six types of edges as shown in the diagram below.

../_images/ACM_schema.png

Input raw node/edge data files#

The raw node and edge data files are both in a parquet format, whose contents are demonstrated as the diagram below.

../_images/ACM_raw_parquet.png

In this example, only the paper nodes have labels and the task is node classification. So, in the JSON file, the paper node has the labels field, and the task_type is specified as classification. Correspondingly, in the paper node parquet file, there is a column, label, stores the label values. All edge types do not have features associated. Therefore, we only have two columns in these parquet files for edges, the source_id and the dest_id.

The configuration JSON file along with these node and edge parquet files are the required inputs of the GraphStorm’s construction tool. Then we can use the tool to create the partition graph data with the following command.

python3 -m graphstorm.gconstruct.construct_graph \
           --conf-file /tmp/acm_raw/config.json \
           --output-dir /tmp/acm_nc \
           --num-parts 1 \
           --graph-name acm

Outputs of graph construction#

The above command reads in the JSON file, and matchs its contents with the node and edge parquet files. It will then read all parquet files, construct the graph, check file correctness, pre-process features, and eventually split the graph into partitions. Outputs of the command will be saved under the /tmp/acm_nc/ folder as followings:

/tmp/acm_nc
acm.json
node_mapping.pt
edge_mapping.pt
|- part0
    edge_feat.dgl
    graph.dgl
    node_feat.dgl

Because the above command specifies the --num-parts to be 1, there is only one partition created, which is saved in the part0 folder. These files become the inputs of GraphStorm’s launch scripts.

Note

Because the parquet format has some limitations, such as only supporting 2 billion elements in a column, etc, we suggest users to use HDF5 format for very large dataset.

Option 2: Required DGL graph#

For some users who are already familiar with DGL, they can convert their graph data into the required DGL graph format. And then use GraphStorm’s partition tools to create the inputs of GraphStorm’s launch scripts.

Required DGL graph format#

  • a dgl.heterograph.

  • All nodes/edges features are set in nodes/edges’ data field, and remember the feature names, which will be used in the later steps.

    • For nodes’ features, the common way to set features is like g.nodes['nodetypename'].data['featurename']=nodefeaturetensor, The formal explanation of DGL’s node feature could be found in the Using node features.

    • For edges’ features, the common way to set features is like g.edges['edgetypename'].data['featurename']=edgefeaturetensor, The formal explanation of DGL’s edge feature could be found in the Using edge features.

  • Save labels (for node/edge tasks) into the target nodes/edges as a feature, and remember the label feature names, which will be used in the later steps.

    • The common way to set node-related labels as a feature is like g.nodes['predictnodetypename'].data['labelname']=nodelabeltensor.

    • The common way to set edge-related labels as a feature is like g.nodes['predictedgetypename'].data['labelname']=edgelabeltensor.

    • For link prediction task, a common way to extract labels is to use existing edges as the positive edges and use negative sampling method to extract non-exist edges as negative edges. So in this step, we do not need to set the labels. The GraphStorm has implemented this function.

  • (Optional) if you have your own train/validation/test split on nodes/edges, you can put the train/validation/test nodes/edges index tensors as three nodes/edges features with the feature names as train_mask, val_mask, and test_mask. If you do not have nodes/edges split, you can use the split functions provided in the GraphStorm partition tools to create them in the next step.

    • For training nodes, the setting is like g.nodes['predictnodetypename'].data['train_mask']=trainingnodeindexetensor.

    • For validation nodes, the setting is like g.nodes['predictnodetypename'].data['val_mask']=validationnodeindexetensor. Make sure you use ‘val_mask’ as the feature name because the GSF uses this name by default.

    • For validation nodes, the setting is like g.nodes['predictnodetypename'].data['test_mask']=testnodeindexetensor.

    • Similar to nodes splits, you can use the same feature names, train_mask, val_mask, and test_mask, to assign the edge index tensors.

    • The index tensor is either a boolean tensor, or an integer tensor including only 0s and 1s.

Once this DGL graph is constructed, you can use DGL’s save_graphs() function to save it into a local file. The file name must follow GraphStorm convention: <datasetname>.dgl. You can give your graph dataset a name, e.g., acm or ogbn_mag.

The ACM graph data example#

For the ACM data, the following command can create a DGL graph as the input for GraphStorm’s partition tools.

python3 -m /graphstorm/examples/acm_data.py \
           --output-type dgl \
           --output-path /tmp/acm_dgl

The below image show how the built DGL ACM data looks like.

../_images/ACM_graph_schema.png ../_images/ACM_LabelAndMask.png

Partition the DGL ACM graph for node classification#

GraphStorm provides two graph partition tools, the partition_graph.py for node/edge prediction graph partition, and the partition_graph_lp.py for the link prediction graph partition.

The below command partition the DGL ACM graph, the acm.dgl in the /tmp/acm_dgl folder, into one partition, and save the partitioned data to /tmp/acm_nc/ folder.

python3 /graphstorm/tools/partition_graph.py \
        --dataset acm\
        --filepath /tmp/acm_dgl \
        --num_parts 1 \
        --predict_ntype paper \
        --nlabel_field paper:label \
        --output /tmp/acm_nc

Outputs of the command are under the /tmp/acm_nc/ folder with the same contents as the Option 1.

Please refer to Graph Partition Configurations to find more details of the arguments of the two partition tools.

Step 2: Modify the YAML configuration file to include your own data’s information#

It is common that users will copy and reuse GraphStorm’s built-in scripts and yaml files to run training/inference on their own graph data, but forget to change the contents of yaml files to match their own data. Below are some parameters that users need to double check and make changes accordingly.

  • part_config: please change value of this parameter to where you store the partitioned graph’s JSON , e.g. the /tmp/acm_nc/acm.json. It is better to use an absolute path to avoid path mis-match.

  • ip_config: please make sure the ip_list.txt created and the path of the ip_list.txt file is correct.

  • node_feat_name: if some types of nodes have features, please make sure to specify these feature names in either the YAML file or use an argument in the launch command. Otherwise, GraphStorm will ignore any features the nodes might have, hence only using learnable embeddings as their features.

For Classification/Regression tasks:

  • label_field: please change values of this field to specify the field name of labeled data in your graph data.

  • num_classes: please change values of this filed to specify the number of classes to be predicted in your graph data if doing a Classification` task.

For Node Classification/Regression tasks:

  • target_ntype: please change values of this field to the node type that the label is associated, which should be the same node type for prediction.

For Edge Classification/Regression tasks:

  • target_etype: please change values of this field to the edge type that the label is associated, which should be the same edge type for prediction.

For Link Prediction tasks:

  • train_etype: please specify values of this field for the edge type that you want to do link prediction for the downstream task, e.g. recommendation or search. Although if not specified, i.e. put None as the value, all edge types will be used for training, this might not commonly used in practice for most Link Prediction related tasks.

  • eval_etype: it is highly recommended that you set this value to be the same as the value of train_etype, so that the evaluation metric can truly demonstrate the performance of models.

Besides these parameters, it is also important for you to use the correct format to configure node/edge types in the YAML files. For example, in an edge-related task, you should provide a canonical edge type, e.g. user,write,paper (no white spaces in this string), for edge types, rather than the edge name only, e.g. the write.

For more detailed information of these parameters, please refer to the GraphStorm Training and Inference Configurations page.

An example ACM YAML file for node classification#

Below is an example YAML configuration file for the ACM data, which sets to use GraphStorm’s built-in RGCN model for node classification on the paper nodes. The YAML file can also be found at the /graphstorm/examples/use_your_own_data/acm_nc.yaml.

---
version: 1.0
gsf:
basic:
    model_encoder_type: rgcn
    backend: gloo
    verbose: false
gnn:
    fanout: "50,50"
    num_layers: 2
    hidden_size: 256
    use_mini_batch_infer: false
input:
    restore_model_path: null
output:
    save_model_path: /tmp/acm_nc/models
    save_embeds_path: /tmp/acm_nc/embeds
hyperparam:
    dropout: 0.
    lr: 0.0001
    lm_tune_lr: 0.0001
    num_epochs: 200
    batch_size: 1024
    bert_infer_bs: 128
    wd_l2norm: 0
    alpha_l2norm: 0.
rgcn:
    num_bases: -1
    use_self_loop: true
    sparse_optimizer_lr: 1e-2
    use_node_embeddings: false
node_classification:
    node_feat_name:
      - "paper:feat"
      - "author:feat"
      - "subject:feat"
    target_ntype: "paper"
    label_field: "label"
    multilabel: false
    num_classes: 14

You can copy this file to the /tmp folder within the GraphStorm container for the next step.

Step 3: Launch training script on your own graphs#

With the partitioned data and configuration YAML file available, it is easy to use GraphStorm’s training scripts to launch the training job.

Note

We assume an ip_list.txt file has been created in the /tmp/ folder. Users can use the following commands to create this file.

touch /tmp/ip_list.txt
echo 127.0.0.1 > /tmp/ip_list.txt

Below is a launch script example that trains a GraphStorm built-in RGCN model on the ACM data for node classification.

python3 -m graphstorm.run.gs_node_classification \
        --workspace /tmp \
        --part-config /tmp/acm_nc/acm.json \
        --ip-config /tmp/ip_list.txt \
        --num-trainers 4 \
        --num-servers 1 \
        --num-samplers 0 \
        --ssh-port 2222 \
        --cf /tmp/acm_nc.yaml \
        --save-model-path /tmp/acm_nc/models \
        --node-feat-name paper:feat author:feat subject:feat

Similar to the Quick-Start tutorial, users can launch the inference script on their own data. Below is the customized scripts for predicting the classes of nodes in the test set of the ACM graph.

python3 -m graphstorm.run.gs_node_classification \
           --inference \
           --workspace /tmp \
           --part-config /tmp/acm_nc/acm.json \
           --ip-config /tmp/ip_list.txt \
           --num-trainers 4 \
           --num-servers 1 \
           --num-samplers 0 \
           --ssh-port 2222 \
           --cf /tmp/acm_nc.yaml \
           --node-feat-name paper:feat author:feat subject:feat \
           --restore-model-path /tmp/acm_nc/models/epoch-0 \
           --save-prediction-path  /tmp/acm_nc/predictions
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