Sampling Performance Testing

benchmarks/cugraph/standalone/bulk_sampling/.gitignore

@@ -0,0 +1 @@
+mg_utils/

benchmarks/cugraph/standalone/bulk_sampling/README.md

@@ -1,11 +1,13 @@
-# cuGraph Bulk Sampling
+# cuGraph Sampling Benchmarks
 
-## Overview
+## cuGraph Bulk Sampling
+
+### Overview
 The `cugraph_bulk_sampling.py` script runs the bulk sampler for a variety of datasets, including
 both generated (rmat) datasets and disk (ogbn_papers100M, etc.) datasets.  It can also load
 replicas of these datasets to create a larger benchmark (i.e. ogbn_papers100M x2).
 
-## Arguments
+### Arguments
 The script takes a variety of arguments to control sampling behavior.
 Required:
     --output_root
@@ -51,14 +53,8 @@ Optional:
         Seed for random number generation.
         Defaults to '62'
 
-    --persist
-        Whether to aggressively use persist() in dask to make the ETL steps (NOT PART OF SAMPLING) faster.
-        Will probably make this script finish sooner at the expense of memory usage, but won't affect
-        sampling time.
-        Changing this is not recommended unless you know what you are doing.
-        Defaults to False.
 
-## Input Format
+### Input Format
 The script expects its input data in the following format:
 ```
 <top level directory>
@@ -103,14 +99,64 @@ the parquet files.  It must have the following format:
 }
 ```
 
-## Output Meta
+### Output Meta
 The script, in addition to the samples, will also output a file named `output_meta.json`.
 This file contains various statistics about the sampling run, including the runtime,
 as well as information about the dataset and system that the samples were produced from.
 
 This metadata file can be used to gather the results from the sampling and training stages
 together.
 
-## Other Notes
+### Other Notes
 For rmat datasets, you will need to generate your own bogus features in the training stage.
 Since that is trivial, that is not done in this sampling script.
+
+## cuGraph MNMG Training
+
+### Overview
+The script `run_train_job.sh` runs with the `sbatch` command to launch a series of slurm jobs.
+First, for a given number of epochs, the script will produce samples for a given graph.
+Then, the training process starts where samples are loaded and training iterations are
+processed.
+
+### Important Notes
+Downloading the dataset files before running the slurm jobs is highly recommended.  Even though
+the script will attempt to download the files if they are not available, this can often
+lead to a timeout which will crash the scripts.  This applies regardless of whether you are training
+with native PyG or cuGraph-PyG.  You can download data as follows:
+
+```
+from ogb.nodeproppred import NodePropPredDataset
+dataset = NodePropPredDataset('ogbn-papers100M', root='/home/username/datasets')
+```
+
+For datasets other than ogbn-papers100M, you follow the same process but only change the dataset name.
+The dataset will be correctly preprocessed when you run training.  In case you have a slow system, you
+can also run preprocessing by running the training script on a single worker, which will avoid a timeout
+which crashes the script.
+
+The multi-GPU utilities are in `mg_utils` in the top level of the cuGraph repository.  You should either
+copy them to this directory or symlink to them before running the scripts.
+
+### Arguments
+You will need to modify the bash scripts to run appopriately for your environment and
+desired training workflow.  The standard sbatch arguments are at the top of the script, such as
+job name, queue, etc.  These will need to be modified for your SLURM cluster.
+
+Next are arguments for the container image (required),
+and directories where the data and outputs are stored.  The directories default to subdirectories
+of the current working directory.  But if there is a high-throughput storage system available,
+using that storage for the samples and datasets is highly recommended.
+
+Next are standard GNN training arguments such as `FANOUT`, `BATCH_SIZE`, etc.  You can also set
+the number of training epochs here.  These are followed by the `REPLICATION_FACTOR` argument, which
+can be used to create replications of the dataset for scale testing purposes.
+
+The final two arguments are `FRAMEWORK` which can be either "cuGraphPyG" or "PyG", and `GPUS_PER_NODE`
+which must be set to the correct value, even if this is provided by a SLURM argument.  If `GPUS_PER_NODE`
+is not set to the correct number of GPUs, the script will hang indefinitely until it times out.  Mismatched
+GPUs per node is currently unsupported by this script but should be possible in practice.
+
+### Output
+The results of training will be outputted to the logs directory with an `output.txt` file for each worker.
+These will be overwritten upon each run.  Accuracy is only reported on rank 0.

benchmarks/cugraph/standalone/bulk_sampling/bench_cugraph_training.py

@@ -0,0 +1,251 @@
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+os.environ["RAPIDS_NO_INITIALIZE"] = "1"
+os.environ["CUDF_SPILL"] = "1"
+os.environ["LIBCUDF_CUFILE_POLICY"] = "KVIKIO"
+os.environ["KVIKIO_NTHREADS"] = "8"
+
+import argparse
+import json
+import warnings
+
+import torch
+import numpy as np
+import pandas
+
+import torch.distributed as dist
+
+from datasets import OGBNPapers100MDataset
+
+from cugraph.testing.mg_utils import enable_spilling
+
+
+def init_pytorch_worker(rank: int, use_rmm_torch_allocator: bool = False) -> None:
+    import cupy
+    import rmm
+    from pynvml.smi import nvidia_smi
+
+    smi = nvidia_smi.getInstance()
+    pool_size = 16e9  # FIXME calculate this
+
+    rmm.reinitialize(
+        devices=[rank],
+        pool_allocator=True,
+        initial_pool_size=pool_size,
+    )
+
+    if use_rmm_torch_allocator:
+        warnings.warn(
+            "Using the rmm pytorch allocator is currently unsupported."
+            " The default allocator will be used instead."
+        )
+        # FIXME somehow get the pytorch allocator to work
+        # from rmm.allocators.torch import rmm_torch_allocator
+        # torch.cuda.memory.change_current_allocator(rmm_torch_allocator)
+
+    from rmm.allocators.cupy import rmm_cupy_allocator
+
+    cupy.cuda.set_allocator(rmm_cupy_allocator)
+
+    cupy.cuda.Device(rank).use()
+    torch.cuda.set_device(rank)
+
+    # Pytorch training worker initialization
+    torch.distributed.init_process_group(backend="nccl")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--gpus_per_node",
+        type=int,
+        default=8,
+        help="# GPUs per node",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--num_epochs",
+        type=int,
+        default=1,
+        help="Number of training epochs",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=512,
+        help="Batch size",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--fanout",
+        type=str,
+        default="10_10_10",
+        help="Fanout",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--sample_dir",
+        type=str,
+        help="Directory with stored bulk samples (required for cuGraph run)",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--output_file",
+        type=str,
+        help="File to store results",
+        required=True,
+    )
+
+    parser.add_argument(
+        "--framework",
+        type=str,
+        help="The framework to test (PyG, cuGraphPyG)",
+        required=True,
+    )
+
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="GraphSAGE",
+        help="The model to use (currently only GraphSAGE supported)",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--replication_factor",
+        type=int,
+        default=1,
+        help="The replication factor for the dataset",
+        required=False,
+    )
+
+    parser.add_argument(
+        "--dataset_dir",
+        type=str,
+        help="The directory where datasets are stored",
+        required=True,
+    )
+
+    parser.add_argument(
+        "--train_split",
+        type=float,
+        help="The percentage of the labeled data to use for training.  The remainder is used for testing/validation.",
+        default=0.8,
+        required=False,
+    )
+
+    parser.add_argument(
+        "--val_split",
+        type=float,
+        help="The percentage of the testing/validation data to allocate for validation.",
+        default=0.5,
+        required=False,
+    )
+
+    return parser.parse_args()
+
+
+def main(args):
+    import logging
+
+    logging.basicConfig(
+        level=logging.INFO,
+    )
+    logger = logging.getLogger("bench_cugraph_training")
+    logger.setLevel(logging.INFO)
+
+    local_rank = int(os.environ["LOCAL_RANK"])
+    global_rank = int(os.environ["RANK"])
+
+    init_pytorch_worker(
+        local_rank, use_rmm_torch_allocator=(args.framework == "cuGraph")
+    )
+    enable_spilling()
+    print(f"worker initialized")
+    dist.barrier()
+
+    world_size = int(os.environ["SLURM_JOB_NUM_NODES"]) * args.gpus_per_node
+
+    dataset = OGBNPapers100MDataset(
+        replication_factor=args.replication_factor,
+        dataset_dir=args.dataset_dir,
+        train_split=args.train_split,
+        val_split=args.val_split,
+        load_edge_index=(args.framework == "PyG"),
+    )
+
+    if global_rank == 0:
+        dataset.download()
+    dist.barrier()
+
+    fanout = [int(f) for f in args.fanout.split("_")]
+
+    if args.framework == "PyG":
+        from trainers.pyg import PyGNativeTrainer
+
+        trainer = PyGNativeTrainer(
+            model=args.model,
+            dataset=dataset,
+            device=local_rank,
+            rank=global_rank,
+            world_size=world_size,
+            num_epochs=args.num_epochs,
+            shuffle=True,
+            replace=False,
+            num_neighbors=fanout,
+            batch_size=args.batch_size,
+        )
+    elif args.framework == "cuGraphPyG":
+        sample_dir = os.path.join(
+            args.sample_dir,
+            f"ogbn_papers100M[{args.replication_factor}]_b{args.batch_size}_f{fanout}",
+        )
+        from trainers.pyg import PyGCuGraphTrainer
+
+        trainer = PyGCuGraphTrainer(
+            model=args.model,
+            dataset=dataset,
+            sample_dir=sample_dir,
+            device=local_rank,
+            rank=global_rank,
+            world_size=world_size,
+            num_epochs=args.num_epochs,
+            shuffle=True,
+            replace=False,
+            num_neighbors=fanout,
+            batch_size=args.batch_size,
+        )
+    else:
+        raise ValueError("unsupported framework")
+
+    logger.info(f"Trainer ready on rank {global_rank}")
+    stats = trainer.train()
+    logger.info(stats)
+
+    with open(f"{args.output_file}[{global_rank}]", "w") as f:
+        json.dump(stats, f)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)