mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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Sage Moore
GitHub
parent
25221b44bb
commit
b4054c8ab4
@ -1,73 +0,0 @@
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#!/usr/bin/env bash
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set -euxo pipefail
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# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
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THRESHOLD=${1:-0.25}
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NUM_Q=${2:-1319}
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PORT=${3:-8030}
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OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
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mkdir -p "${OUT_DIR}"
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wait_for_server() {
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local port=$1
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timeout 600 bash -c '
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until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
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sleep 1
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done'
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}
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MODEL="deepseek-ai/DeepSeek-V2-lite"
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# Set BACKENDS based on platform
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if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
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# ROCm platform
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BACKENDS=("allgather_reducescatter")
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# Disable MOE padding for ROCm since it is causing eplb to fail
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export VLLM_ROCM_MOE_PADDING=0
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else
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# Non-ROCm platform (CUDA/other)
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BACKENDS=("deepep_high_throughput" "deepep_low_latency")
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fi
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cleanup() {
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if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
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kill "${SERVER_PID}" 2>/dev/null || true
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for _ in {1..20}; do
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kill -0 "${SERVER_PID}" 2>/dev/null || break
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sleep 0.5
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done
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kill -9 "${SERVER_PID}" 2>/dev/null || true
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fi
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}
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trap cleanup EXIT
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for BACK in "${BACKENDS[@]}"; do
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VLLM_DEEP_GEMM_WARMUP=skip \
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VLLM_ALL2ALL_BACKEND=$BACK \
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vllm serve "$MODEL" \
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--enforce-eager \
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--tensor-parallel-size 2 \
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--data-parallel-size 2 \
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--enable-expert-parallel \
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--enable-eplb \
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--eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
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--trust-remote-code \
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--max-model-len 2048 \
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--port $PORT &
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SERVER_PID=$!
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wait_for_server $PORT
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TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
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OUT="${OUT_DIR}/${TAG}_${BACK}_async_eplb.json"
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python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
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python3 - <<PY
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import json; acc=json.load(open('${OUT}'))['accuracy']
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print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
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assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
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PY
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cleanup
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SERVER_PID=
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sleep 1
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PORT=$((PORT+1))
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done
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@ -50,7 +50,6 @@ for BACK in "${BACKENDS[@]}"; do
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--data-parallel-size 2 \
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--enable-expert-parallel \
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--enable-eplb \
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--eplb-config '{"window_size":200,"step_interval":600}' \
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--trust-remote-code \
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--max-model-len 2048 \
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--port $PORT &
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@ -1,74 +0,0 @@
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#!/usr/bin/env bash
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set -euxo pipefail
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# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
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THRESHOLD=${1:-0.25}
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NUM_Q=${2:-1319}
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PORT=${3:-8040}
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OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
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mkdir -p "${OUT_DIR}"
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wait_for_server() {
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local port=$1
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timeout 600 bash -c '
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until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
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sleep 1
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done'
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}
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MODEL="Qwen/Qwen3-Next-80B-A3B-Instruct"
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# Set BACKENDS based on platform
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if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
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# ROCm platform
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BACKENDS=("allgather_reducescatter")
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# Disable MOE padding for ROCm since it is causing eplb to fail
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export VLLM_ROCM_MOE_PADDING=0
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else
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# Non-ROCm platform (CUDA/other)
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BACKENDS=("deepep_high_throughput" "deepep_low_latency")
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fi
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cleanup() {
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if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
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kill "${SERVER_PID}" 2>/dev/null || true
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for _ in {1..20}; do
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kill -0 "${SERVER_PID}" 2>/dev/null || break
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sleep 0.5
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done
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kill -9 "${SERVER_PID}" 2>/dev/null || true
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fi
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}
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trap cleanup EXIT
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for BACK in "${BACKENDS[@]}"; do
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VLLM_DEEP_GEMM_WARMUP=skip \
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VLLM_ALL2ALL_BACKEND=$BACK \
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vllm serve "$MODEL" \
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--enforce-eager \
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--tensor-parallel-size 4 \
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--enable-expert-parallel \
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--enable-eplb \
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--eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
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--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
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--trust-remote-code \
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--max-model-len 2048 \
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--gpu-memory-utilization 0.9 \
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--port $PORT &
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SERVER_PID=$!
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wait_for_server $PORT
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TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
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OUT="${OUT_DIR}/${TAG}_${BACK}.json"
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python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
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python3 - <<PY
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import json; acc=json.load(open('${OUT}'))['accuracy']
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print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
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assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
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PY
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cleanup
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SERVER_PID=
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sleep 1
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PORT=$((PORT+1))
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done
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@ -1379,22 +1379,4 @@ steps:
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num_gpus: 2
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working_dir: "/vllm-workspace"
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commands:
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- bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
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- label: DeepSeek V2-Lite Async EPLB Accuracy
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timeout_in_minutes: 60
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gpu: h100
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optional: true
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num_gpus: 4
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working_dir: "/vllm-workspace"
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commands:
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- bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh 0.25 1319 8030
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- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy
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timeout_in_minutes: 60
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gpu: h100
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optional: true
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num_gpus: 4
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working_dir: "/vllm-workspace"
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commands:
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- bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040
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- bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
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@ -322,6 +322,9 @@ async def transfer_layer(
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num_local_physical_experts = next(iter(expert_weights[0])).shape[0]
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assert new_global_expert_indices.shape == (num_moe_layers, num_physical_experts)
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assert num_physical_experts == ep_size * num_local_physical_experts
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# A buffer to hold the expert weights in one layer during the exchange.
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# NOTE: Currently we assume the same weights across different layers
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# have the same shape.
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is_unchanged, is_received_locally, experts_recv_loc = move_to_buffer(
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num_local_experts=num_local_physical_experts,
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