mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
97 lines
3.1 KiB
Bash
97 lines
3.1 KiB
Bash
#!/bin/bash
|
|
#SBATCH -A <account>
|
|
#SBATCH -p <partition>
|
|
#SBATCH -t 01:00:00
|
|
#SBATCH -N 2
|
|
#SBATCH --ntasks-per-node=8
|
|
#SBATCH -o logs/trtllm-bench.out
|
|
#SBATCH -e logs/trtllm-bench.err
|
|
#SBATCH -J trtllm-bench
|
|
|
|
### Run trtllm-bench with pytorch backend on Slurm
|
|
|
|
# NOTE, this feature is experimental and may not work on all systems.
|
|
# The trtllm-llmapi-launch is a script that launches the LLM-API code on
|
|
# Slurm-like systems, and can support multi-node and multi-GPU setups.
|
|
|
|
# Note that, the number of MPI processes should be the same as the model world
|
|
# size. e.g. For tensor_parallel_size=16, you may use 2 nodes with 8 gpus for
|
|
# each, or 4 nodes with 4 gpus for each or other combinations.
|
|
|
|
# This docker image should have tensorrt_llm installed, or you need to install
|
|
# it in the task.
|
|
|
|
# The following variables are expected to be set in the environment:
|
|
# You can set them via --export in the srun/sbatch command.
|
|
# CONTAINER_IMAGE: the docker image to use, you'd better install tensorrt_llm in it, or install it in the task.
|
|
# MOUNT_DIR: the directory to mount in the container
|
|
# MOUNT_DEST: the destination directory in the container
|
|
# WORKDIR: the working directory in the container
|
|
# SOURCE_ROOT: the path to the TensorRT-LLM source
|
|
# PROLOGUE: the prologue to run before the script
|
|
# LOCAL_MODEL: the local model directory to use, NOTE: downloading from HF is
|
|
# not supported in Slurm mode, you need to download the model and put it in
|
|
# the LOCAL_MODEL directory.
|
|
|
|
export prepare_dataset="$SOURCE_ROOT/benchmarks/cpp/prepare_dataset.py"
|
|
export data_path="$WORKDIR/token-norm-dist.txt"
|
|
|
|
echo "Preparing dataset..."
|
|
srun -l \
|
|
-N 1 \
|
|
-n 1 \
|
|
--container-image=${CONTAINER_IMAGE} \
|
|
--container-name="prepare-name" \
|
|
--container-mounts=${MOUNT_DIR}:${MOUNT_DEST} \
|
|
--container-workdir=${WORKDIR} \
|
|
--export=ALL \
|
|
--mpi=pmix \
|
|
bash -c "
|
|
$PROLOGUE
|
|
python3 $prepare_dataset \
|
|
--tokenizer=$LOCAL_MODEL \
|
|
--stdout token-norm-dist \
|
|
--num-requests=100 \
|
|
--input-mean=128 \
|
|
--output-mean=128 \
|
|
--input-stdev=0 \
|
|
--output-stdev=0 > $data_path
|
|
"
|
|
|
|
echo "Running benchmark..."
|
|
# Just launch trtllm-bench job with trtllm-llmapi-launch command.
|
|
|
|
srun -l \
|
|
--container-image=${CONTAINER_IMAGE} \
|
|
--container-mounts=${MOUNT_DIR}:${MOUNT_DEST} \
|
|
--container-workdir=${WORKDIR} \
|
|
--export=ALL,PYTHONPATH=${SOURCE_ROOT} \
|
|
--mpi=pmix \
|
|
bash -c "
|
|
set -ex
|
|
$PROLOGUE
|
|
export PATH=$PATH:~/.local/bin
|
|
|
|
# This is optional
|
|
cat > /tmp/pytorch_extra_args.txt << EOF
|
|
pytorch_backend_config:
|
|
use_cuda_graph: false
|
|
enable_overlap_scheduler: true
|
|
cuda_graph_padding_enabled: false
|
|
print_iter_log: true
|
|
enable_attention_dp: false
|
|
EOF
|
|
|
|
# launch the benchmark
|
|
trtllm-llmapi-launch \
|
|
trtllm-bench \
|
|
--model $MODEL_NAME \
|
|
--model_path $LOCAL_MODEL \
|
|
throughput \
|
|
--dataset $data_path \
|
|
--backend pytorch \
|
|
--tp 16 \
|
|
--extra_llm_api_options /tmp/pytorch_extra_args.txt \
|
|
$EXTRA_ARGS
|
|
"
|