mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-13 22:18:36 +08:00
b7a255d67e
Signed-off-by: Yan Chunwei <328693+Superjomn@users.noreply.github.com> Signed-off-by: Mike Iovine <6158008+mikeiovine@users.noreply.github.com> Signed-off-by: Mike Iovine <miovine@nvidia.com>
95 lines
3.9 KiB
Bash
95 lines
3.9 KiB
Bash
#!/bin/bash
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#SBATCH -A <account> # parameter
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#SBATCH -p <partition> # parameter
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#SBATCH -t 01:00:00
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#SBATCH -N 1
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#SBATCH --ntasks-per-node=2
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#SBATCH -o logs/llmapi-distributed.out
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#SBATCH -e logs/llmapi-distributed.err
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#SBATCH -J llmapi-distributed-task
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##############################################################################
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# OVERVIEW:
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# This script demonstrates running a custom LLM API Python script on SLURM
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# with distributed inference support. It executes quickstart_advanced.py with
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# tensor parallelism across multiple GPUs/nodes.
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#
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# WHAT TO MODIFY:
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# 1. SLURM Parameters (lines 2-9):
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# - Replace <account> with your SLURM account name
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# - Replace <partition> with your SLURM partition name
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# - Adjust -N (number of nodes) based on your TP size
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# - Adjust --ntasks-per-node (GPUs per node) to match your setup
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#
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# 2. Environment Variables (set before running sbatch):
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# - CONTAINER_IMAGE: Docker image with TensorRT-LLM installed
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# - MOUNT_DIR: Host directory to mount in container
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# - MOUNT_DEST: Container mount destination path
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# - WORKDIR: Working directory inside container
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# - SOURCE_ROOT: Path to TensorRT-LLM source code
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# - PROLOGUE: Commands to run before main task (e.g., module loads)
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# - LOCAL_MODEL: Path to your pre-downloaded model directory
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#
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# 3. Script Configuration (lines 39, 51-54):
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# - Line 39: Change $script to point to your own Python script
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# - Line 52: Modify --model_dir to use your model path
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# - Line 53: Customize --prompt with your test prompt
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# - Line 54: Adjust --tp_size to match your node/GPU setup
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#
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# EXAMPLE USAGE:
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# export CONTAINER_IMAGE="nvcr.io/nvidia/tensorrt_llm:latest"
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# export LOCAL_MODEL="/path/to/llama-model"
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# sbatch llm_mgmn_llm_distributed.sh
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#
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# NOTE: This is a template - you can replace quickstart_advanced.py with any
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# LLM API Python script. The trtllm-llmapi-launch wrapper handles the
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# distributed execution setup automatically.
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##############################################################################
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### :section Slurm
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### :title Run LLM-API with pytorch backend on Slurm
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### :order 0
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# NOTE, this feature is experimental and may not work on all systems.
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# The trtllm-llmapi-launch is a script that launches the LLM-API code on
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# Slurm-like systems, and can support multi-node and multi-GPU setups.
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# IMPORTANT: Total MPI processes (nodes × ntasks-per-node) must equal tp_size.
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# e.g. For tensor_parallel_size=16, you may use 2 nodes with 8 gpus for
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# each, or 4 nodes with 4 gpus for each or other combinations.
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# This docker image should have tensorrt_llm installed, or you need to install
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# it in the task.
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# The following variables are expected to be set in the environment:
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# You can set them via --export in the srun/sbatch command.
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# CONTAINER_IMAGE: the docker image to use, you'd better install tensorrt_llm in it, or install it in the task.
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# MOUNT_DIR: the directory to mount in the container
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# MOUNT_DEST: the destination directory in the container
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# WORKDIR: the working directory in the container
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# SOURCE_ROOT: the path to the TensorRT LLM source
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# PROLOGUE: the prologue to run before the script
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# LOCAL_MODEL: the local model directory to use, NOTE: downloading from HF is
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# not supported in Slurm mode, you need to download the model and put it in
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# the LOCAL_MODEL directory.
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# Adjust the paths to run
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export script=$SOURCE_ROOT/examples/llm-api/quickstart_advanced.py
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# Just launch the PyTorch example with trtllm-llmapi-launch command.
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srun -l \
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--container-image=${CONTAINER_IMAGE} \
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--container-mounts=${MOUNT_DIR}:${MOUNT_DEST} \
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--container-workdir=${WORKDIR} \
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--export=ALL \
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--mpi=pmix \
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bash -c "
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$PROLOGUE
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export PATH=$PATH:~/.local/bin
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trtllm-llmapi-launch python3 $script \
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--model_dir $LOCAL_MODEL \
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--prompt 'Hello, how are you?' \
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--tp_size 2 \
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--max_batch_size 256
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"
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