TensorRT-LLMs/examples/wide_ep/slurm_scripts/README.md

TensorRT-LLM Wide-EP Benchmark Scripts

This directory contains scripts for benchmarking TensorRT-LLM wide-ep performance using SLURM job scheduler.

⚠️ DISCLAIMER

These scripts are currently not QA'ed and are provided for demonstration purposes only.

Please note that:

• These scripts have not undergone formal quality assurance testing
• They are intended for demonstration and educational purposes
• Use at your own risk in production environments
• Always review and test scripts thoroughly before running in your specific environment

Scripts Overview

Core Scripts

1. submit.sh - Main entry point for submitting benchmark jobs
2. disaggr_torch.slurm - SLURM job script orchestrating the entire benchmark
3. gen_yaml.py - Generates configuration files for serving setup
4. start_server.sh - Starts the inference server
5. start_worker.sh - Starts the worker processes
6. run_benchmark.sh - Executes the benchmark workload
7. process_gen_iterlog.py - Processes benchmark results and generates reports

Usage

Prerequisites

Before running the scripts, ensure you have:

• Access to a SLURM cluster
• Container image with TensorRT-LLM installed
• Model files accessible on the cluster
• Required environment variables set

Configuration

Edit the following variables in submit.sh and disaggr_torch.slurm:

# In disaggr_torch.slurm
container_image=${container_image}     # Your container image
mount_dir=${mount_dir}                 # Mount directory path
model_dir=${model_dir}                 # Model directory path

Running Benchmarks

1. Submit benchmark jobs:

   ./submit.sh
   

2. Monitor job progress:

   squeue -u $USER
   

3. View results: Results are saved in bm_20250703_deepseek-r1-{isl}-{osl}/ directory

Script Details

submit.sh

Main entry script that submits multiple SLURM jobs with different configurations:

• DEP8: 8-way parallelism for decode servers
• DEP16: 16-way parallelism with different EPLB slot configurations
• DEP32: 32-way parallelism for high-throughput scenarios

Parameters tested:

• Concurrency levels: 1x, 64x, 1024x multipliers
• EPLB slots: 0, 256, 288
• Different parallelism sizes

disaggr_torch.slurm

SLURM job script that:

1. Sets up container environment
2. Generates configuration files
3. Starts server and workers
4. Executes benchmarks
5. Cleans up processes

Key parameters:

• num_ctx_servers: Number of context servers
• ctx_tp_size: Tensor parallel size for context servers
• num_gen_servers: Number of generation servers
• gen_tp_size: Tensor parallel size for generation servers
• concurrency: Number of concurrent requests

gen_yaml.py

Generates YAML configuration files with:

• Server topology and resource allocation
• Network configuration (hostnames, ports)
• Memory and batch size settings
• Optimization parameters (CUDA graphs, KV cache)

Key features:

• Automatic node and task allocation
• Support for attention data parallelism
• MoE load balancing configuration
• Speculative decoding (MTP) support

start_server.sh & start_worker.sh

• Server: Starts the main inference server with API endpoint
• Workers: Starts MPI workers for distributed processing
• Support for profiling with NSight Systems
• Environment variable configuration for optimizations

run_benchmark.sh

Executes benchmarking using TensorRT-LLM's benchmark_serving tool:

• Downloads ShareGPT dataset for realistic workloads
• Waits for server health checks
• Runs load testing with specified concurrency
• Collects performance metrics
• Gracefully shuts down services

Metrics collected:

• Throughput (tokens/second)
• Latency (request completion time)
• Context vs generation only statistics

process_gen_iterlog.py

Post-processes benchmark results:

• Parses iteration logs from workers
• Calculates throughput metrics
• Generates CSV reports
• Supports MTP (Multi-Token Prediction) analysis