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The output of python collect_env.py
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.7.0+cu126
Is debug build : False
CUDA used to build PyTorch : 12.6
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.11.11 | packaged by conda-forge | (main, Mar 3 2025, 20:43:55) [GCC 13.3.0] (64-bit runtime)
Python platform : Linux-6.5.0-1024-aws-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.4.131
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3
Nvidia driver version : 550.163.01
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 192
On-line CPU(s) list: 0-191
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7R13 Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
Stepping: 1
BogoMIPS: 5299.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 3 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 48 MiB (96 instances)
L3 cache: 384 MiB (12 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-47,96-143
NUMA node1 CPU(s): 48-95,144-191
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] mypy==1.7.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==26.0.3
[pip3] torch==2.7.0
[pip3] torchaudio==2.7.0
[pip3] torchvision==0.22.0
[pip3] transformers==4.51.3
[pip3] triton==3.3.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
[conda] pyzmq 26.0.3 pypi_0 pypi
[conda] torch 2.7.0 pypi_0 pypi
[conda] torchaudio 2.7.0 pypi_0 pypi
[conda] torchvision 0.22.0 pypi_0 pypi
[conda] transformers 4.51.3 pypi_0 pypi
[conda] triton 3.3.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.9.2.dev50+g017ef648e (git sha: 017ef648e)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 0-47,96-143 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 0-47,96-143 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 0-47,96-143 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 0-47,96-143 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 48-95,144-191 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 48-95,144-191 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 48-95,144-191 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X 48-95,144-191 1 N/A
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
NCCL_VERSION=2.21.5-1
NCCL_SOCKET_IFNAME=^lo,docker,veth,tailscale,anyscale
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
CUDA_VERSION=12.4.1
LD_LIBRARY_PATH=/usr/local/ucx/lib:/usr/local/nixl/lib/x86_64-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
So I am seeing that the NIXL handshake can take up to 9 sec for my system on H100s in a 4P2D scenario.
This can add up if the requests that are batched together come from different prefill nodes.
Imagine a 32P1D situation, and I sent a 32 concurrent requests where r1 -> p1, r2 -> p2, ..., r32 -> p32.
Then when they all hit D, the handshake happens during the first fwd pass and can take 32 * 9 = 288 seconds.
The issue manifests itself when we use ray compiled graph as there is a timeout set to ~300s (or at least the intention is with this PR to set it to 300s by default). This puts the engine in a brittle situation if you do not warm up the system gradually. The workaround right now is to warm up in a way that does not trigger timeout. Making sure each D knows about all the Ps before sending requests.
This issue will come up again when you do aggressive autoscaling on Decode and the newly added decode instance should know about all the Ps before hand.
#!/bin/bash
set -xe
# Models to run
MODELS=(
"Qwen/Qwen2.5-0.5B-Instruct"
)
export VLLM_LOGGING_LEVEL=debug
# Number of prefill and decode instances to create
NUM_PREFILL_INSTANCES=${NUM_PREFILL_INSTANCES:-2} # Default to 1
NUM_DECODE_INSTANCES=${NUM_DECODE_INSTANCES:-1} # Default to 2
# Find the git repository root directory
# GIT_ROOT=$(git rev-parse --show-toplevel)
SMI_BIN=$(which nvidia-smi || which rocm-smi)
# Trap the SIGINT signal (triggered by Ctrl+C)
trap 'kill $(jobs -pr)' SIGINT SIGTERM EXIT
# Waits for vLLM to start.
wait_for_server() {
local port=$1
timeout 1200 bash -c "
until curl -s localhost:${port}/v1/completions > /dev/null; do
sleep 1
done" && return 0 || return 1
}
# Handle to get model-specific arguments for deepseek
get_model_args() {
local model_name=$1
local extra_args=""
if [[ "$model_name" == "deepseek-ai/deepseek-vl2-tiny" ]]; then
extra_args="--hf_overrides '{\"architectures\": [\"DeepseekVLV2ForCausalLM\"]}' --trust-remote-code"
fi
echo "$extra_args"
}
get_num_gpus() {
if [[ "$SMI_BIN" == *"nvidia"* ]]; then
echo "$($SMI_BIN --query-gpu=name --format=csv,noheader | wc -l)"
else
echo "$($SMI_BIN -l | grep GPU | wc -l)"
fi
}
# Function to run tests for a specific model
run_tests_for_model() {
local model_name=$1
echo "================================"
echo "Testing model: $model_name"
echo "================================"
# Get model-specific arguments
local model_args=$(get_model_args "$model_name")
# Arrays to store all hosts and ports
PREFILL_HOSTS=()
PREFILL_PORTS=()
DECODE_HOSTS=()
DECODE_PORTS=()
# Start prefill instances
for i in $(seq 0 $((NUM_PREFILL_INSTANCES-1))); do
# Calculate GPU ID - we'll distribute across available GPUs
GPU_ID=$((i % $(get_num_gpus)))
# GPU_ID=3
# Calculate port number (base port + instance number)
PORT=$((9570 + i))
# Calculate side channel port
SIDE_CHANNEL_PORT=$((4000 + i))
echo "Starting prefill instance $i on GPU $GPU_ID, port $PORT"
# Build the command with or without model-specific args
BASE_CMD="CUDA_VISIBLE_DEVICES=$GPU_ID VLLM_NIXL_SIDE_CHANNEL_PORT=$SIDE_CHANNEL_PORT vllm serve $model_name \
--port $PORT \
--disable-log-requests \
--gpu-memory-utilization 0.9 \
--kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'"
if [ -n "$model_args" ]; then
FULL_CMD="$BASE_CMD $model_args"
else
FULL_CMD="$BASE_CMD"
fi
eval "$FULL_CMD &"
# Store host and port for proxy configuration
PREFILL_HOSTS+=("localhost")
PREFILL_PORTS+=($PORT)
done
# Start decode instances
for i in $(seq 0 $((NUM_DECODE_INSTANCES-1))); do
# Calculate GPU ID - we'll distribute across available GPUs, starting from after prefill GPUs
GPU_ID=$(((i + NUM_PREFILL_INSTANCES) % $(get_num_gpus)))
# GPU_ID=4
# Calculate port number (base port + instance number)
PORT=$((9560 + i))
# Calculate side channel port
SIDE_CHANNEL_PORT=$((4100 + i))
echo "Starting decode instance $i on GPU $GPU_ID, port $PORT"
# Build the command with or without model-specific args
BASE_CMD="CUDA_VISIBLE_DEVICES=$GPU_ID VLLM_NIXL_SIDE_CHANNEL_PORT=$SIDE_CHANNEL_PORT vllm serve $model_name \
--port $PORT \
--disable-log-requests \
--gpu-memory-utilization 0.9 \
--kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'"
if [ -n "$model_args" ]; then
FULL_CMD="$BASE_CMD $model_args"
else
FULL_CMD="$BASE_CMD"
fi
eval "$FULL_CMD &"
# Store host and port for proxy configuration
DECODE_HOSTS+=("localhost")
DECODE_PORTS+=($PORT)
done
# Wait for all instances to start
for PORT in "${PREFILL_PORTS[@]}"; do
echo "Waiting for prefill instance on port $PORT to start..."
wait_for_server $PORT
done
for PORT in "${DECODE_PORTS[@]}"; do
echo "Waiting for decode instance on port $PORT to start..."
wait_for_server $PORT
done
# Build the command for the proxy server with all the hosts and ports
PROXY_CMD="python ./toy_proxy_server.py --port 8192"
# Add all prefill hosts and ports
PROXY_CMD+=" --prefiller-hosts ${PREFILL_HOSTS[@]}"
PROXY_CMD+=" --prefiller-ports ${PREFILL_PORTS[@]}"
# Add all decode hosts and ports
PROXY_CMD+=" --decoder-hosts ${DECODE_HOSTS[@]}"
PROXY_CMD+=" --decoder-ports ${DECODE_PORTS[@]}"
# Start the proxy server
echo "Starting proxy server with command: $PROXY_CMD"
$PROXY_CMD &
# Wait for the proxy to start
# Run lm eval for this model
echo "Running tests for $model_name"
sleep 10000
# TEST_MODEL=$model_name python -m pytest -s -x ${GIT_ROOT}/tests/v1/kv_connector/nixl_integration/test_accuracy.py
# # Clean up before running next model
# cleanup_instances
# sleep 3
}
# Run tests for each model
for model in "${MODELS[@]}"; do
run_tests_for_model "$model"
done
# echo "All tests completed!"You can run the above script via NUM_PREFILL_INSTANCES=4 NUM_DECODE_INSTANCES=2 RAY_DEDUP_LOGS=0 bash run_vllm_xpyd.sh 2>&1 | tee deploy_vllm_4p2d.log and see the debug statements of NIXL handshake: get metadata took: <>.
I then run the vllm benchmark scripts at concurrency 32 for 10k requests. It's a bit hard to repro but the above is what could happen.
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