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[CI][NIXL] Isolate concurrent engine internal ports (#49129)

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Signed-off-by: Andreas Karatzas <Andreas.Karatzas@amd.com>
Co-authored-by: OpenAI Codex <noreply@openai.com>
This commit is contained in:
Andreas Karatzas
2026-07-20 22:28:11 -05:00
committed by GitHub
co-authored by OpenAI Codex
parent ea0e9c8f2e
commit 6bcda970fd
1 changed files with 13 additions and 0 deletions
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tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh
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@@ -83,6 +83,11 @@ DECODE_BLOCK_SIZE=${DECODE_BLOCK_SIZE:-128}
ENFORCE_EAGER=${ENFORCE_EAGER:-1}
# Comma-separated extra args for vllm serve (e.g. --max-model-len,2048)
VLLM_SERVE_EXTRA_ARGS=${VLLM_SERVE_EXTRA_ARGS:-}
# Pin concurrent prefiller and non-DP decoder engines to separate internal
# port windows. DP decoder ranks retain their existing internal port selection.
PREFILLER_INTERNAL_PORT_BASE=${PREFILLER_INTERNAL_PORT_BASE:-20000}
DECODER_INTERNAL_PORT_BASE=${DECODER_INTERNAL_PORT_BASE:-30000}
INTERNAL_PORT_STRIDE=${INTERNAL_PORT_STRIDE:-100}
# Resolve the repository root from the script location instead of `.git`.
# The ROCm CI image copies `/vllm-workspace` without the Git metadata, so
@@ -154,12 +159,14 @@ run_tests_for_model() {
PORT=$((8100 + i))
# Calculate side channel port. Avoid clash with with TP workers.
SIDE_CHANNEL_PORT=$((5559 + i))
INTERNAL_PORT=$((PREFILLER_INTERNAL_PORT_BASE + i * INTERNAL_PORT_STRIDE))
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_KV_CACHE_LAYOUT='HND' \
VLLM_PORT=$INTERNAL_PORT \
UCX_NET_DEVICES=all \
VLLM_NIXL_SIDE_CHANNEL_PORT=$SIDE_CHANNEL_PORT \
vllm serve $model_name \
@@ -208,12 +215,18 @@ run_tests_for_model() {
PORT=$((8200 + i))
# Calculate side channel port
SIDE_CHANNEL_PORT=$((5659 + i * $DECODER_TP_SIZE))
INTERNAL_PORT=$((DECODER_INTERNAL_PORT_BASE + i * INTERNAL_PORT_STRIDE))
DECODER_INTERNAL_PORT_ENV=
if [[ -z "${DP_EP:-}" ]]; then
DECODER_INTERNAL_PORT_ENV="VLLM_PORT=$INTERNAL_PORT"
fi
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_KV_CACHE_LAYOUT=$DECODER_KV_LAYOUT \
$DECODER_INTERNAL_PORT_ENV \
UCX_NET_DEVICES=all \
VLLM_NIXL_SIDE_CHANNEL_PORT=$SIDE_CHANNEL_PORT \
vllm serve $model_name \