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Multi-Host HLO Runner

[TOC]

This tool lets you run an HLO module on one or more GPUs. It also allows compiling code targeting multiple GPUs without running it.

Running multi-GPU (sharded) HLOs

We can identify these HLOs by seeing sharding= annotations. For example sharding={devices=[1,1,2,1]0,1} means that the annotated tensor should be sharded to 2 GPUs (GPU0 and GPU1) along the 3rd dimension.

The following instructions assume the working directory is the XLA Git repository and that ./configure.py has been run.

If we have enough GPUs, we can replay these HLOs like this:

bazel run //xla/tools/multihost_hlo_runner:hlo_runner_main -- my-hlo.txt

Tip: If the input generation takes too long or uses too much host memory, consider using --hlo_argument_mode=uninitialized.

It is also possible to compile the same HLO without running it by setting --run=false:

bazel run //xla/tools/multihost_hlo_runner:hlo_runner_main -- --run=false my-hlo.txt

In that case, a single GPU is necessary, unless the autotuning cache is used.

Troubleshooting

  • Errors such as Check failed: result.replicas >= 1 (0 vs. 1):
    • We have to make sure that we have enough GPUs.
    • CUDA_VISIBLE_DEVICES must be set correctly or not set at all.
  • Crashes:
    • We may want to use --dynamic_mode=off.
    • CUDA and cuDNN should be set up correctly.

Examples

Single process, multiple GPU example

Setup and get the HLO

You can use a container with the following instructions:

  docker run -it --shm-size=1g --gpus all ghcr.io/nvidia/jax:pax-2024-06-03
  cd /opt/xla/

Note, those instructions can be outdated more quickly. Adjust as needed.

# The 8 below is the number of GPUs you have.
# test-pax.sh --help for more details on the parallelization options
(export XLA_FLAGS="--xla_dump_to=/tmp/dump"; test-pax.sh --fsdp 8 --batch-per-gpu 1)

ls -lSh /tmp/dump/*before_optimizations.txt
# The biggest file one is normally the one you care about.
# I picked one, for the rest of the scripts, but the name could change when you change the JAX or XLA version.

Build XLA multihost runner

cd /opt/xla/
./configure.py --backend CUDA --nccl
bazel build //xla/tools/multihost_hlo_runner:hlo_runner_main

Single process example: Before optimization graph replay

bazel run //xla/tools/multihost_hlo_runner:hlo_runner_main -- \
  /tmp/dump/module_0023.pjit__wrapped_step_fn.before_optimizations.txt

Single process example: After optimization graph replay

To replay an optimized HLO, you must use either --xla_disable_all_hlo_passes or --run_xla_backend_only. Otherwise, XLA will try to recompile the HLO and this isn't supported. So it will give you many strange errors.

Full command: bazel run //xla/tools/multihost_hlo_runner:hlo_runner_main -- --run_xla_backend_only /tmp/dump/module_0023.pjit__wrapped_step_fn.sm_8.0_gpu_after_optimizations.txt

Multi-processes, single-node

Launch container

Also install some missing libraries. (Note, that can be outdated more quickly. Adjust as needed.)

docker run -it --shm-size=1g --gpus all ghcr.io/nvidia/jax:pax-2024-06-03
apt-get update && apt-get install -y openmpi-bin openmpi-common libopenmpi-dev

Run original model and dump HLO

For this example, we will use an 8-GPU PAXML model from test-pax.sh. (Note this will be the same dump as the single process case. So you can do cp -r /tmp/dump /tmp/dump_multi_process if you already have it. export XLA_FLAGS="--xla_dump_to=/tmp/dump_multi_process" mpirun --allow-run-as-root -np 8 test-pax.sh --fsdp 8 --batch-per-gpu 1 -o /tmp/checkpoint --multiprocess

The HLO dump will be saved to /tmp/dump_multi_process/. For PAX specifically, the main module will have "pjit__wrapped_step_fn" in the name. For this example we will use /tmp/dump_multi_process/module_0023.pjit__wrapped_step_fn.before_optimizations.txt.

Run on a single node using MPI

Create a bash script called run.sh:

#!/bin/bash
export CUDA_VISIBLE_DEVICES=${OMPI_COMM_WORLD_LOCAL_RANK}
bazel run //xla/tools/multihost_hlo_runner:hlo_runner_main -- \
  --task_id=${OMPI_COMM_WORLD_RANK} \
  --num_nodes=${OMPI_COMM_WORLD_SIZE} \
  --address=127.0.0.1:12345 \
  /tmp/dump_multi_process/module_0023.pjit__wrapped_step_fn.before_optimizations.txt

Now, you can execute it using mpirun:

chmod a+x run.sh
mpirun --allow-run-as-root -np 8 run.sh

Run on multiple nodes with SLURM

When running on multiple nodes using SLURM, you can forward the SLURM env variables to the HLO runner like so in your SLURM job:

bazel run //xla/tools/multihost_hlo_runner:hlo_runner_main -- \
  --task_id=${SLURM_PROCID} \
  --num_nodes=${SLURM_NTASKS} \
  --address="${SLURM_LAUNCH_NODE_IPADDR}:12345" \
  /tmp/dump_multi_process/module_0023.pjit__wrapped_step_fn.before_optimizations.txt